Robots operating in domestic environments generally need to interact with articulated objects, su... more Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of ap...
In this paper, we consider the problem of learning two-dimensional spatial models of gas distribu... more In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms...
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.
In this paper, we consider the problem of learning a two dimensional spatial model of a gas distr... more In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties of typical gas distributions, we propose a sparse Gaussian process mixture model. This allows us to accurately represent the smooth background signal as well as areas of high concentration. We integrate the sparsification of the training data into an EM procedure used for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. We demonstrate that our models are well suited for predicting gas concentrations at new query locations and that they outperform alternative methods used in robotics to carry out in this task.
In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localiza... more In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localization and mapping problem by applying stochastic gradient descent to minimize the error introduced by constraints. Together with multi-level relaxation, this is one of the most robust and efficient maximum likelihood techniques published so far. In this paper, we present an extension of Olson's algorithm. It applies a novel parameterization of the nodes in the graph that significantly improves the performance and enables us to cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory as it is the case with both previous approaches. We implemented our technique and compared it to multi-level relaxation and Olson's algorithm. As we demonstrate in simulated and in real world experiments, our approach converges faster than the other approaches and yields accurate maps of the environment.
Probabilistic robotics is a new and growing area in robotics, concerned with perception and contr... more Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. Probabilistic approaches have been discovered as one of the most powerful ways to address highly relevant problems in mobile robotics, including robot state estimation and localization. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. Robots are inherently uncertain about the state of their environments. Uncertainty arises from sensor limitations, noise, and the fact that most interesting environments are-to a certain degree-unpredictable. When "guessing" a quantity from sensor data, the probabilistic approach computes a probability distribution over what might be the case in the world, instead of generating a single "best guess" only. As a result, a robot using probabilistic methods can gracefully recover from errors, handle ambiguities, and integrate sensor data in a consistent way. And, moreover, it knows about its own ignorance. Autonomous robots must act in the face of uncertainty-a direct consequence of their inability to know what is the case. When making decisions, probabilistic approaches take the robot's uncertainty into account. Some consider only the robot's current uncertainty, others anticipate future uncertainty. Instead of considering the most likely situations only (current or projected), many probabilistic approaches strive to compute a decision-theoretic optimum, in which decisions are based on all possible contingencies. This tutorial will discuss both aspects and present recently developed techniques based on particle filters for estimating the position of a mobile robot and for efficiently learning a map of an unknown environment with a mobile robot. We will furthermore describe how graphbased representations can be utilized to find maximum likelihood solutions. We will finally describe how the complexity of this state estimation problem can be reduced by actively controlling the vehicle. For all algorithms, experimental results will be presented that have been obtained with mobile robots in real-world environments.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor... more In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor setup which consists of two standard web cams and a low cost inertial measurement unit. This setup is designed for lightweight or flying robots. Our technique uses visual features extracted from the web cams and estimates the 3D location of the landmarks via stereo vision. Feature correspondences are estimated using a variant of the PROSAC algorithm. Our mapping technique constructs a graph of spatial constraints and applies an efficient gradient descent-based optimization approach to estimate the most likely map of the environment. Our approach has been evaluated in comparably large outdoor and indoor environments. We furthermore present experiments in which our technique is applied to build a map with a blimp.
In this paper, we address the problem of robot navigation in environments with deformable objects... more In this paper, we address the problem of robot navigation in environments with deformable objects. The aim is to include the costs of object deformations when planning the robot's motions and trade them off against the travel costs. We present our recently developed robotic system that is able to acquire deformation models of real objects. The robot determines the elasticity parameters by physical interaction with the object and by establishing a relation between the applied forces and the resulting surface deformations. The learned deformation models can then be used to perform physically realistic finite element simulations. This allows the planner to evaluate robot trajectories and to predict the costs of object deformations. Since finite element simulations are time-consuming, we furthermore present an approach to approximate object-specific deformation cost functions by means of Gaussian process regression. We present two real-world applications of our motion planner for a wheeled robot and a manipulation robot. As we demonstrate in real-world experiments, our system is able to estimate appropriate deformation parameters of real objects that can be used to predict future deformations. We show that our deformation cost approximation improves the efficiency of the planner by several orders of magnitude.
In this paper, we address the problem of creating an objective benchmark for evaluating SLAM appr... more In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.
In this paper, we consider the problem of exploring an unknown environment with a team of mobile ... more In this paper, we consider the problem of exploring an unknown environment with a team of mobile robots. In this context, we assume that the robots have only a low bandwidth communication link. The key problem to be solved in this context is to decide which information should be transmitted over the network to enable the other team mates to choose appropriate target points. In this paper, each robot approximates its representation of the environment by a set of polygons. We present an efficient way to incrementally improve that kind of maps. We furthermore adapt an existing coordination strategy so that it is able to deal with our approximated representations in a distributed fashion. Our technique has been implemented and tested. The results demonstrate that our distributed technique can efficiently spread the robots over the environment even if the communication link provides only a low bandwidth. As a result, the robots are able to quickly accomplish their exploration mission despite the constraints introduced by the limited bandwidth.
Indoor environments can typically be divided into places with different functionalities like corr... more Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that such semantic information enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, or localization. In this paper, we propose an approach to classify places in indoor environments into different categories. Our approach uses AdaBoost to boost simple features extracted from vision and laser range data. Furthermore, we apply a Hidden Markov Model to take spatial dependencies between robot poses into account and to increase the robustness of the classification. Our technique has been implemented and tested on real robots as well as in simulation. Experiments presented in this paper demonstrate that our approach can be utilized to robustly classify places into semantic categories.
Abstract—Robots operating in domestic environments need to deal with a variety of different objec... more Abstract—Robots operating in domestic environments need to deal with a variety of different objects. Often, these objects are neither placed randomly, nor independently of each other. For example, objects on a breakfast table such as plates, knives, or bowls typically occur in recurrent configurations. In this paper, we propose a novel hierarchical generative model to reason about latent object constellations in a scene. The proposed model is a combination of Dirichlet processes and beta processes, which allow for a probabilistic treatment of the ...
Robots operating in domestic environments generally need to interact with articulated objects, su... more Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of ap...
In this paper, we consider the problem of learning two-dimensional spatial models of gas distribu... more In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms...
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.
In this paper, we consider the problem of learning a two dimensional spatial model of a gas distr... more In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties of typical gas distributions, we propose a sparse Gaussian process mixture model. This allows us to accurately represent the smooth background signal as well as areas of high concentration. We integrate the sparsification of the training data into an EM procedure used for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. We demonstrate that our models are well suited for predicting gas concentrations at new query locations and that they outperform alternative methods used in robotics to carry out in this task.
In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localiza... more In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localization and mapping problem by applying stochastic gradient descent to minimize the error introduced by constraints. Together with multi-level relaxation, this is one of the most robust and efficient maximum likelihood techniques published so far. In this paper, we present an extension of Olson's algorithm. It applies a novel parameterization of the nodes in the graph that significantly improves the performance and enables us to cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory as it is the case with both previous approaches. We implemented our technique and compared it to multi-level relaxation and Olson's algorithm. As we demonstrate in simulated and in real world experiments, our approach converges faster than the other approaches and yields accurate maps of the environment.
Probabilistic robotics is a new and growing area in robotics, concerned with perception and contr... more Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. Probabilistic approaches have been discovered as one of the most powerful ways to address highly relevant problems in mobile robotics, including robot state estimation and localization. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. Robots are inherently uncertain about the state of their environments. Uncertainty arises from sensor limitations, noise, and the fact that most interesting environments are-to a certain degree-unpredictable. When "guessing" a quantity from sensor data, the probabilistic approach computes a probability distribution over what might be the case in the world, instead of generating a single "best guess" only. As a result, a robot using probabilistic methods can gracefully recover from errors, handle ambiguities, and integrate sensor data in a consistent way. And, moreover, it knows about its own ignorance. Autonomous robots must act in the face of uncertainty-a direct consequence of their inability to know what is the case. When making decisions, probabilistic approaches take the robot's uncertainty into account. Some consider only the robot's current uncertainty, others anticipate future uncertainty. Instead of considering the most likely situations only (current or projected), many probabilistic approaches strive to compute a decision-theoretic optimum, in which decisions are based on all possible contingencies. This tutorial will discuss both aspects and present recently developed techniques based on particle filters for estimating the position of a mobile robot and for efficiently learning a map of an unknown environment with a mobile robot. We will furthermore describe how graphbased representations can be utilized to find maximum likelihood solutions. We will finally describe how the complexity of this state estimation problem can be reduced by actively controlling the vehicle. For all algorithms, experimental results will be presented that have been obtained with mobile robots in real-world environments.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor... more In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor setup which consists of two standard web cams and a low cost inertial measurement unit. This setup is designed for lightweight or flying robots. Our technique uses visual features extracted from the web cams and estimates the 3D location of the landmarks via stereo vision. Feature correspondences are estimated using a variant of the PROSAC algorithm. Our mapping technique constructs a graph of spatial constraints and applies an efficient gradient descent-based optimization approach to estimate the most likely map of the environment. Our approach has been evaluated in comparably large outdoor and indoor environments. We furthermore present experiments in which our technique is applied to build a map with a blimp.
In this paper, we address the problem of robot navigation in environments with deformable objects... more In this paper, we address the problem of robot navigation in environments with deformable objects. The aim is to include the costs of object deformations when planning the robot's motions and trade them off against the travel costs. We present our recently developed robotic system that is able to acquire deformation models of real objects. The robot determines the elasticity parameters by physical interaction with the object and by establishing a relation between the applied forces and the resulting surface deformations. The learned deformation models can then be used to perform physically realistic finite element simulations. This allows the planner to evaluate robot trajectories and to predict the costs of object deformations. Since finite element simulations are time-consuming, we furthermore present an approach to approximate object-specific deformation cost functions by means of Gaussian process regression. We present two real-world applications of our motion planner for a wheeled robot and a manipulation robot. As we demonstrate in real-world experiments, our system is able to estimate appropriate deformation parameters of real objects that can be used to predict future deformations. We show that our deformation cost approximation improves the efficiency of the planner by several orders of magnitude.
In this paper, we address the problem of creating an objective benchmark for evaluating SLAM appr... more In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.
In this paper, we consider the problem of exploring an unknown environment with a team of mobile ... more In this paper, we consider the problem of exploring an unknown environment with a team of mobile robots. In this context, we assume that the robots have only a low bandwidth communication link. The key problem to be solved in this context is to decide which information should be transmitted over the network to enable the other team mates to choose appropriate target points. In this paper, each robot approximates its representation of the environment by a set of polygons. We present an efficient way to incrementally improve that kind of maps. We furthermore adapt an existing coordination strategy so that it is able to deal with our approximated representations in a distributed fashion. Our technique has been implemented and tested. The results demonstrate that our distributed technique can efficiently spread the robots over the environment even if the communication link provides only a low bandwidth. As a result, the robots are able to quickly accomplish their exploration mission despite the constraints introduced by the limited bandwidth.
Indoor environments can typically be divided into places with different functionalities like corr... more Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that such semantic information enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, or localization. In this paper, we propose an approach to classify places in indoor environments into different categories. Our approach uses AdaBoost to boost simple features extracted from vision and laser range data. Furthermore, we apply a Hidden Markov Model to take spatial dependencies between robot poses into account and to increase the robustness of the classification. Our technique has been implemented and tested on real robots as well as in simulation. Experiments presented in this paper demonstrate that our approach can be utilized to robustly classify places into semantic categories.
Abstract—Robots operating in domestic environments need to deal with a variety of different objec... more Abstract—Robots operating in domestic environments need to deal with a variety of different objects. Often, these objects are neither placed randomly, nor independently of each other. For example, objects on a breakfast table such as plates, knives, or bowls typically occur in recurrent configurations. In this paper, we propose a novel hierarchical generative model to reason about latent object constellations in a scene. The proposed model is a combination of Dirichlet processes and beta processes, which allow for a probabilistic treatment of the ...
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