The use of RGB-D cameras has become an affordable solution for robot mapping and navigation in co... more The use of RGB-D cameras has become an affordable solution for robot mapping and navigation in contrast to expensive 2D laser range finders. Although these sensors provide richer information about the 3D environment, most successful mapping and navigation techniques for mobile robots have been developed considering a 2D planar environment. In this paper, we present our system for 2D navigation using RGB-D sensors. The key feature of our system is the extraction of 2D laser scans out of the 3D point cloud provided by the camera that can be later used by common mapping or localization approaches. Along with the real experiments we raise the question "how far can we go with the use of RGB-D sensors for 2D navigation?" and we analize performance and limitations of the system compared to accurate, yet expensive, laser-based systems.
This paper presents an exploration strategy highly similar to the one included in the proposal of... more This paper presents an exploration strategy highly similar to the one included in the proposal of this project. The exploration strategy presented in this paper also uses a metric and topological map, a 1-step ahead POMDP based planner, and information gain to decide the best exploration action. This paper will also appear this year in a special collection with some of the most relevant papers in Robotics appeared in the last years. C. Stachniss, G. Grisetti, and W. Burgard, "Information Gain-based Exploration Using Rao-Blackwellized Particle Filters". Robotics Science and Systems II, G. Sukhatme, S. Schaal, W. Burgard, and D. Fox (Eds.), MIT press, April 2007. As we detail in this document, to face this new fact, we develop a new exploration strategy that builds upon our previous idea but also proposes a novel algorithm to adjust the perception system of the robot. This new approach uses the current estimation of the hybrid topological-metric map to decide not only which area to explore next, but also which landmarks to add or recognize in the map. We believe that this is a novel and relevant idea. We already developed the theory behind the approach, and we are currently testing our irnplementation arid preparing the main publication that resumes our finding.
In this paper, we present an approach towards mapping and safe navigation in real, large-scale en... more In this paper, we present an approach towards mapping and safe navigation in real, large-scale environments with an autonomous car. The goal is to enable the car to autonomously navigate on roads while avoiding obstacles and while simultaneously learning an accurate three-dimensional model of the environment. To achieve these goals, we apply probabilistic state estimation techniques, network-based pose optimization, and a sensor-based traversability analysis approach. In order to achieve fast map learning, our system compresses the sensor data using multi-level surface maps. The overall systems runs on a modified Smart car equipped with different types of sensors. We present several results obtained from extensive experiments which illustrate the capabilities of our vehicle.
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
As autonomous robots are increasingly being introduced in real-world environments operating for l... more As autonomous robots are increasingly being introduced in real-world environments operating for long periods of time, the difficulties of long-term mapping are attracting the attention of the robotics research community. This paper proposes a full SLAM system capable of handling the dynamics of the environment across a single or multiple mapping sessions. Using the pose graph SLAM paradigm, the system works on local maps in the form of 2D point cloud data which are updated over time to store the most up-to-date state of the environment. The core of our system is an efficient ICP-based alignment and merging procedure working on the clouds that copes with non-static entities of the environment. Furthermore, the system retains the graph complexity by removing outdated nodes upon robust inter-and intra-session loop closure detections while graph coherency is preserved by using condensed measurements. Experiments conducted with real data from longterm SLAM datasets demonstrate the efficiency, accuracy and effectiveness of our system in the management of the mapping problem during long-term robot operation.
The ability to maintain and continuously update geometric calibration parameters of a mobile plat... more The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it and their time delays. In this paper, we present a unified pipeline for motionbased calibration of mobile platforms equipped with multiple heterogeneous sensors. We formulate a unified optimization problem to concurrently estimate the platform kinematic parameters, the sensors extrinsic parameters and their time delays. We analyze the influence of the trajectory followed by the robot on the accuracy of the estimate. Our framework automatically selects appropriate trajectories to maximize the information gathered and to obtain a more accurate parameters estimate. In combination with that, our pipeline observes the parameters evolution in long-term operation to detect possible values change in the parameters set. The experiments conducted on real data show a smooth convergence along with the ability to detect changes in parameters value. We release an open-source version of our framework to the community.
Topic: estimation, control Oral presentation Robots that are able to acquire an accurate model of... more Topic: estimation, control Oral presentation Robots that are able to acquire an accurate model of their environment are regarded as fulfilling a major precondition of truly autonomous mobile vehicles. To learn a map of the environment, three problems need to be addressed simultaneously, namely exploration, mapping, and localization. In this work, we present an integrated solution to these three problems. To solve the simultaneous localization and mapping (SLAM) problem, our approach uses a highly efficient variant of algorithm proposed by Murphy and colleagues [2, 4]. In this algorithm, a Rao-Blackwellized particle filter (RBPF) is used to efficiently represent the joint posterior about possible maps and trajectories taken by the robot. The key contribution of our approach is an efficient decision-theoretic algorithm for computing vantage points that reduce the expected uncertainty in the RBPF. The approaches mostly related to our work have been presented by Makarenko et al. [3] and Bourgault et al. [1]. They use an Extended Kalman Filter (EKF) to solve the SLAM problem and introduce a utility function which trades-off the cost of exploring new terrain with the potential reduction of uncertainty by measuring at selected positions. A similar technique has been applied by Sim et al. [5], who consider actions to guide the robot back to a known place in order to reduce the pose uncertainty of the vehicle during exploration. In contrast to our work, these approaches assume that the environment contains landmarks that can be uniquely determined during mapping. Our approach, in contrast, learns occupancy grid maps and thus is not restricted to environments with pre-defined landmarks. Compared to previous approaches, the novelty of the work reported here is that our algorithm simultaneously considers the uncertainty in the trajectory and in the map while building accurate occupancy grids. Based on an efficient scheme for computing the uncertainty of the joint posterior, we apply decisiontheoretic framework for choosing appropriate actions. Thereby, we utilize the properties of the Rao-Blackwellization. In brief, the uncertainty of an RBPF is determined by its entropy, which consists of two components. Whereas the first one computes the uncertainty over the trajectory taken by the robot, the second calculates the uncertainty of the individual maps weighted by the likelihood of the corresponding particles. We also demonstrate how the filter can be used to efficiently simulate possible measurements to be obtained along a path through already mapped terrain. To determine potential actions, we consider different action types including actions that collect sensor information about unknown areas as well as actions designed to re-visit known places to reduce the pose uncertainty of the vehicle. To finally select an action, we take into account the potential measurements gathered along the path of the robot as well as the cost for carrying out this action. Our approach has been implemented and tested in the real world and in simulation. Experimental results suggest that our approach leads to a robust exploration behavior that produces highly accurate grid maps. Furthermore, we illustrate the advantages of our action selection technique over previous approaches.
Advances in Intelligent Systems and Computing, 2015
RGBD sensors are commonly used in robotics applications for many purposes, including 3D reconstru... more RGBD sensors are commonly used in robotics applications for many purposes, including 3D reconstruction of the environment and mapping. In these tasks, uncalibrated sensors can generate poor quality results. In this article we propose a quick and easy to use approach to estimate the undistortion function of RGBD sensors. Our approach does not rely on the knowledge of the sensor model, on the use of a specific calibration pattern or on external SLAM systems to track the device position. We compute an extensive representation of the undistortion function as well as its statistics and use machine learning methods for approximation of the undistortion function. We validated our approach on datasets acquired from different kinds of RGBD sensors and using a precise 3D ground truth. We also provide a procedure for evaluating the quality of the calibration using a mobile robot and a 2D laser range finder. The results clearly show the advantages in using sensor data calibrated with the method described in this article.
SummaryModels of the environment are needed for a wide range of robotic applications, from search... more SummaryModels of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., ...
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
Pose graphs have become a popular representation for solving the simultaneous localization and ma... more Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.
Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)
The goal of the project, which is currently under development, is to design tools to monitor the ... more The goal of the project, which is currently under development, is to design tools to monitor the situation after a large-scale disaster, with a particular focus on the task on situation assessment and high-level information fusion, as well as on the issues that arise in coordinating the agent actions based on the acquired information. The development environment is based on the RoboCup-Rescue simulator: a simulation environment used for the RoboCup-Rescue competition, allowing for the design of both agents operating in the scenario and simulators for modeling various aspects of the situation including the graphical interface to monitor the disaster site. Our project is focussed on three aspects: modeling in the simulator a scenario devised from the analysis of a real case study; an extension of the simulator enabling for the experimentation of various communication and information fusion schemes; a framework for developing agents that are capable of constructing a global view of the situation and of distributing specific information to other agents in order to drive their actions.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
Learning maps is one of the fundamental tasks of mobile robots. In the past, numerous efficient a... more Learning maps is one of the fundamental tasks of mobile robots. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we consider the problem of learning maps with mobile robots that operate in non-flat environments and apply maximum likelihood techniques to solve the graph-based SLAM problem. Due to the non-commutativity of the rotational angles in 3D, major problems arise when applying approaches designed for the two-dimensional world. The non-commutativity introduces serious difficulties when distributing a rotational error over a sequence of poses. In this paper, we present an efficient solution to the SLAM problem that is able to distribute a rotational error over a sequence of nodes. Our approach applies a variant of gradient descent to solve the error minimization problem. We implemented our technique and tested it on large simulated and real world datasets. We furthermore compared our approach to solving the problem by LU-decomposition. As the experiments illustrate, our technique converges significantly faster to an accurate map with low error and is able to correct maps with bigger noise than existing methods.
2010 IEEE International Conference on Robotics and Automation, 2010
The problem of place recognition appears in different mobile robot navigation problems including ... more The problem of place recognition appears in different mobile robot navigation problems including localization, SLAM, or change detection in dynamic environments. Whereas this problem has been studied intensively in the context of robot vision, relatively few approaches are available for threedimensional range data. In this paper, we present a novel and robust method for place recognition based on range images. Our algorithm matches a given 3D scan against a database using point features and scores potential transformations by comparing significant points in the scans. A further advantage of our approach is that the features allow for a computation of the relative transformations between scans which is relevant for registration processes. Our approach has been implemented and tested on different 3D data sets obtained outdoors. In several experiments we demonstrate the advantages of our approach also in comparison to existing techniques.
One important design decision for the development of autonomously navigating mobile robots is the... more One important design decision for the development of autonomously navigating mobile robots is the choice of the representation of the environment. This includes the question which type of features should be used or whether a dense representation such as occupancy grid maps is more appropriate. In this paper, we present an approach which performs SLAM using multiple representations of the environment simultaneously. It uses reinforcement to learn when to switch to an alternative representation method depending on the current observation. This allows the robot to update its pose and map estimate based on the representation that models the surrounding of the robot in the best way. The approach has been implemented on a real robot and evaluated in scenarios, in which a robot has to navigate in-and outdoors and therefore switches between a landmark-based representation and a dense grid map. In practical experiments, we demonstrate that our approach allows a robot to robustly map environments which cannot be adequately modeled by either of the individual representations.
Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localizat... more Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao-Blackwellized particle filters.
The use of RGB-D cameras has become an affordable solution for robot mapping and navigation in co... more The use of RGB-D cameras has become an affordable solution for robot mapping and navigation in contrast to expensive 2D laser range finders. Although these sensors provide richer information about the 3D environment, most successful mapping and navigation techniques for mobile robots have been developed considering a 2D planar environment. In this paper, we present our system for 2D navigation using RGB-D sensors. The key feature of our system is the extraction of 2D laser scans out of the 3D point cloud provided by the camera that can be later used by common mapping or localization approaches. Along with the real experiments we raise the question "how far can we go with the use of RGB-D sensors for 2D navigation?" and we analize performance and limitations of the system compared to accurate, yet expensive, laser-based systems.
This paper presents an exploration strategy highly similar to the one included in the proposal of... more This paper presents an exploration strategy highly similar to the one included in the proposal of this project. The exploration strategy presented in this paper also uses a metric and topological map, a 1-step ahead POMDP based planner, and information gain to decide the best exploration action. This paper will also appear this year in a special collection with some of the most relevant papers in Robotics appeared in the last years. C. Stachniss, G. Grisetti, and W. Burgard, "Information Gain-based Exploration Using Rao-Blackwellized Particle Filters". Robotics Science and Systems II, G. Sukhatme, S. Schaal, W. Burgard, and D. Fox (Eds.), MIT press, April 2007. As we detail in this document, to face this new fact, we develop a new exploration strategy that builds upon our previous idea but also proposes a novel algorithm to adjust the perception system of the robot. This new approach uses the current estimation of the hybrid topological-metric map to decide not only which area to explore next, but also which landmarks to add or recognize in the map. We believe that this is a novel and relevant idea. We already developed the theory behind the approach, and we are currently testing our irnplementation arid preparing the main publication that resumes our finding.
In this paper, we present an approach towards mapping and safe navigation in real, large-scale en... more In this paper, we present an approach towards mapping and safe navigation in real, large-scale environments with an autonomous car. The goal is to enable the car to autonomously navigate on roads while avoiding obstacles and while simultaneously learning an accurate three-dimensional model of the environment. To achieve these goals, we apply probabilistic state estimation techniques, network-based pose optimization, and a sensor-based traversability analysis approach. In order to achieve fast map learning, our system compresses the sensor data using multi-level surface maps. The overall systems runs on a modified Smart car equipped with different types of sensors. We present several results obtained from extensive experiments which illustrate the capabilities of our vehicle.
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
As autonomous robots are increasingly being introduced in real-world environments operating for l... more As autonomous robots are increasingly being introduced in real-world environments operating for long periods of time, the difficulties of long-term mapping are attracting the attention of the robotics research community. This paper proposes a full SLAM system capable of handling the dynamics of the environment across a single or multiple mapping sessions. Using the pose graph SLAM paradigm, the system works on local maps in the form of 2D point cloud data which are updated over time to store the most up-to-date state of the environment. The core of our system is an efficient ICP-based alignment and merging procedure working on the clouds that copes with non-static entities of the environment. Furthermore, the system retains the graph complexity by removing outdated nodes upon robust inter-and intra-session loop closure detections while graph coherency is preserved by using condensed measurements. Experiments conducted with real data from longterm SLAM datasets demonstrate the efficiency, accuracy and effectiveness of our system in the management of the mapping problem during long-term robot operation.
The ability to maintain and continuously update geometric calibration parameters of a mobile plat... more The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it and their time delays. In this paper, we present a unified pipeline for motionbased calibration of mobile platforms equipped with multiple heterogeneous sensors. We formulate a unified optimization problem to concurrently estimate the platform kinematic parameters, the sensors extrinsic parameters and their time delays. We analyze the influence of the trajectory followed by the robot on the accuracy of the estimate. Our framework automatically selects appropriate trajectories to maximize the information gathered and to obtain a more accurate parameters estimate. In combination with that, our pipeline observes the parameters evolution in long-term operation to detect possible values change in the parameters set. The experiments conducted on real data show a smooth convergence along with the ability to detect changes in parameters value. We release an open-source version of our framework to the community.
Topic: estimation, control Oral presentation Robots that are able to acquire an accurate model of... more Topic: estimation, control Oral presentation Robots that are able to acquire an accurate model of their environment are regarded as fulfilling a major precondition of truly autonomous mobile vehicles. To learn a map of the environment, three problems need to be addressed simultaneously, namely exploration, mapping, and localization. In this work, we present an integrated solution to these three problems. To solve the simultaneous localization and mapping (SLAM) problem, our approach uses a highly efficient variant of algorithm proposed by Murphy and colleagues [2, 4]. In this algorithm, a Rao-Blackwellized particle filter (RBPF) is used to efficiently represent the joint posterior about possible maps and trajectories taken by the robot. The key contribution of our approach is an efficient decision-theoretic algorithm for computing vantage points that reduce the expected uncertainty in the RBPF. The approaches mostly related to our work have been presented by Makarenko et al. [3] and Bourgault et al. [1]. They use an Extended Kalman Filter (EKF) to solve the SLAM problem and introduce a utility function which trades-off the cost of exploring new terrain with the potential reduction of uncertainty by measuring at selected positions. A similar technique has been applied by Sim et al. [5], who consider actions to guide the robot back to a known place in order to reduce the pose uncertainty of the vehicle during exploration. In contrast to our work, these approaches assume that the environment contains landmarks that can be uniquely determined during mapping. Our approach, in contrast, learns occupancy grid maps and thus is not restricted to environments with pre-defined landmarks. Compared to previous approaches, the novelty of the work reported here is that our algorithm simultaneously considers the uncertainty in the trajectory and in the map while building accurate occupancy grids. Based on an efficient scheme for computing the uncertainty of the joint posterior, we apply decisiontheoretic framework for choosing appropriate actions. Thereby, we utilize the properties of the Rao-Blackwellization. In brief, the uncertainty of an RBPF is determined by its entropy, which consists of two components. Whereas the first one computes the uncertainty over the trajectory taken by the robot, the second calculates the uncertainty of the individual maps weighted by the likelihood of the corresponding particles. We also demonstrate how the filter can be used to efficiently simulate possible measurements to be obtained along a path through already mapped terrain. To determine potential actions, we consider different action types including actions that collect sensor information about unknown areas as well as actions designed to re-visit known places to reduce the pose uncertainty of the vehicle. To finally select an action, we take into account the potential measurements gathered along the path of the robot as well as the cost for carrying out this action. Our approach has been implemented and tested in the real world and in simulation. Experimental results suggest that our approach leads to a robust exploration behavior that produces highly accurate grid maps. Furthermore, we illustrate the advantages of our action selection technique over previous approaches.
Advances in Intelligent Systems and Computing, 2015
RGBD sensors are commonly used in robotics applications for many purposes, including 3D reconstru... more RGBD sensors are commonly used in robotics applications for many purposes, including 3D reconstruction of the environment and mapping. In these tasks, uncalibrated sensors can generate poor quality results. In this article we propose a quick and easy to use approach to estimate the undistortion function of RGBD sensors. Our approach does not rely on the knowledge of the sensor model, on the use of a specific calibration pattern or on external SLAM systems to track the device position. We compute an extensive representation of the undistortion function as well as its statistics and use machine learning methods for approximation of the undistortion function. We validated our approach on datasets acquired from different kinds of RGBD sensors and using a precise 3D ground truth. We also provide a procedure for evaluating the quality of the calibration using a mobile robot and a 2D laser range finder. The results clearly show the advantages in using sensor data calibrated with the method described in this article.
SummaryModels of the environment are needed for a wide range of robotic applications, from search... more SummaryModels of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., ...
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
Pose graphs have become a popular representation for solving the simultaneous localization and ma... more Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.
Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)
The goal of the project, which is currently under development, is to design tools to monitor the ... more The goal of the project, which is currently under development, is to design tools to monitor the situation after a large-scale disaster, with a particular focus on the task on situation assessment and high-level information fusion, as well as on the issues that arise in coordinating the agent actions based on the acquired information. The development environment is based on the RoboCup-Rescue simulator: a simulation environment used for the RoboCup-Rescue competition, allowing for the design of both agents operating in the scenario and simulators for modeling various aspects of the situation including the graphical interface to monitor the disaster site. Our project is focussed on three aspects: modeling in the simulator a scenario devised from the analysis of a real case study; an extension of the simulator enabling for the experimentation of various communication and information fusion schemes; a framework for developing agents that are capable of constructing a global view of the situation and of distributing specific information to other agents in order to drive their actions.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
Learning maps is one of the fundamental tasks of mobile robots. In the past, numerous efficient a... more Learning maps is one of the fundamental tasks of mobile robots. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we consider the problem of learning maps with mobile robots that operate in non-flat environments and apply maximum likelihood techniques to solve the graph-based SLAM problem. Due to the non-commutativity of the rotational angles in 3D, major problems arise when applying approaches designed for the two-dimensional world. The non-commutativity introduces serious difficulties when distributing a rotational error over a sequence of poses. In this paper, we present an efficient solution to the SLAM problem that is able to distribute a rotational error over a sequence of nodes. Our approach applies a variant of gradient descent to solve the error minimization problem. We implemented our technique and tested it on large simulated and real world datasets. We furthermore compared our approach to solving the problem by LU-decomposition. As the experiments illustrate, our technique converges significantly faster to an accurate map with low error and is able to correct maps with bigger noise than existing methods.
2010 IEEE International Conference on Robotics and Automation, 2010
The problem of place recognition appears in different mobile robot navigation problems including ... more The problem of place recognition appears in different mobile robot navigation problems including localization, SLAM, or change detection in dynamic environments. Whereas this problem has been studied intensively in the context of robot vision, relatively few approaches are available for threedimensional range data. In this paper, we present a novel and robust method for place recognition based on range images. Our algorithm matches a given 3D scan against a database using point features and scores potential transformations by comparing significant points in the scans. A further advantage of our approach is that the features allow for a computation of the relative transformations between scans which is relevant for registration processes. Our approach has been implemented and tested on different 3D data sets obtained outdoors. In several experiments we demonstrate the advantages of our approach also in comparison to existing techniques.
One important design decision for the development of autonomously navigating mobile robots is the... more One important design decision for the development of autonomously navigating mobile robots is the choice of the representation of the environment. This includes the question which type of features should be used or whether a dense representation such as occupancy grid maps is more appropriate. In this paper, we present an approach which performs SLAM using multiple representations of the environment simultaneously. It uses reinforcement to learn when to switch to an alternative representation method depending on the current observation. This allows the robot to update its pose and map estimate based on the representation that models the surrounding of the robot in the best way. The approach has been implemented on a real robot and evaluated in scenarios, in which a robot has to navigate in-and outdoors and therefore switches between a landmark-based representation and a dense grid map. In practical experiments, we demonstrate that our approach allows a robot to robustly map environments which cannot be adequately modeled by either of the individual representations.
Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localizat... more Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao-Blackwellized particle filters.
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Papers by G. Grisetti