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69
Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing
- In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’06
, 2006
"... Abstract — To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used i ..."
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Cited by 85 (20 self)
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Abstract — To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used in the past are elevation maps which store in each cell of a discrete grid the height of the surface in the corresponding area. Whereas elevation maps provide a compact representation, they lack the ability to represent vertical structures or even multiple levels. In this paper, we propose a new representation denoted as multi-level surface maps (MLS maps). Our approach allows to store multiple surfaces in each cell of the grid. This enables a mobile robot to model environments with structures like bridges, underpasses, buildings or mines. Additionally, they allow to represent vertical structures. Throughout this paper we present algorithms for updating these maps based on sensory input, to match maps calculated from two different scans, and to solve the loop-closing problem given such maps. Experiments carried out with a real robot in an outdoor environment demonstrate that our approach is well-suited for representing large-scale outdoor environments. I.
An efficient extension of elevation maps for outdoor terrain mapping
- In Proc. of the Int. Conf. on Field and Service Robotics (FSR
, 2005
"... Elevation maps are a popular data structure for representing the environment of a mobile robot operating outdoors or on not-flat surfaces. Elevation maps store in each cell of a discrete grid the height of the surface at the corresponding place in the environment. However, the use of this 2 1-dimens ..."
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Cited by 40 (8 self)
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Elevation maps are a popular data structure for representing the environment of a mobile robot operating outdoors or on not-flat surfaces. Elevation maps store in each cell of a discrete grid the height of the surface at the corresponding place in the environment. However, the use of this 2 1-dimensional representation, is disad-2 vantageous when utilized for mapping with mobile robots operating on the ground, since vertical or overhanging objects cannot be represented appropriately. Furthermore, such objects can lead to registration errors when two elevation maps have to be matched. In this paper, we propose an approach that allows a mobile robot to deal with vertical and overhanging objects in elevation maps. Our approach classifies the points in the environment according to whether they correspond to such objects or not. We also present a variant of the ICP algorithm that utilizes the classification of cells during the data association. Additionally, we describe how the constraints computed by the ICP algorithm can be applied to determine globally consistent alignments. Experiments carried out with a real robot in an outdoor environment demonstrate that our approach yields highly accurate elevation maps even in the case of loops. We furthermore present experimental results demonstrating that our classification increases the robustness of the scan matching process. 1
Metric-based scan matching algorithms for mobile robot displacement estimation
- In Int. Conf. on Robotics and Automation
, 2005
"... Abstract — This paper presents a metric-based matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The contribution is a geometric distance that takes into account the translation and orientation of the sensor at the same time. This result is us ..."
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Cited by 23 (0 self)
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Abstract — This paper presents a metric-based matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The contribution is a geometric distance that takes into account the translation and orientation of the sensor at the same time. This result is used in the two steps of the matching- estimation process. The correspondences between scans are established with this measure and the minimization of the error is also carried out in terms of this distance. As a result, the translation and rotation are compensated in this framework simultaneously. In fact, this is the contribution with respect to previous work that addressed only translation or translation and rotation but separately. The new technique has been implemented and tested on a real vehicle. The experiments illustrate how it is more robust and accurate than prior techniques. At the end of the paper, we give an extension of our distance measure to 3D range-data matching problems. I.
Vision based modeling and localization for planetary exploration rovers
- 55th International Astronautical Congress 2004
, 2004
"... Exploration of large unknown planetary environments will rely on rovers that can autonomously cover distances of kilometres and maintain precise information about their location with respect to local features. During such traversals, the rovers will create photo-realistic three dimensional (3D) mode ..."
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Cited by 23 (1 self)
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Exploration of large unknown planetary environments will rely on rovers that can autonomously cover distances of kilometres and maintain precise information about their location with respect to local features. During such traversals, the rovers will create photo-realistic three dimensional (3D) models of visited sites for autonomous operations on-site and mission planning on Earth. Currently rover position is estimated using wheel odometry, which is sufficient for short traversals but as error accumulates quickly, it is unsuitable for long distances. At MD Robotics, we are working on imaging technologies for future planetary rover missions. Two complementary technologies are currently investigated: a stereo based vision system and a scanning time-of-flight LIDAR system. Both imaging systems have been installed on board of two experimental rovers and tested in laboratory and outdoor environments. With stereo cameras, the rover can create photo-realistic 3D model as well as provide visual odometry that is more accurate than the rover dead reckoning. With the LIDAR, the rover can match 3D scans to estimate the relative location to improve the wheel and visual odometry. 1
Model-based executive control through reactive planning for autonomous rovers
- in IROS 2004 (IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2004
"... Abstract — This paper reports on the design and implementation of a real-time executive for a mobile rover that uses a model-based, declarative approach. The control system is based on the Intelligent Distributed Execution Architecture (IDEA), an approach to planning and execution that provides a un ..."
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Cited by 21 (7 self)
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Abstract — This paper reports on the design and implementation of a real-time executive for a mobile rover that uses a model-based, declarative approach. The control system is based on the Intelligent Distributed Execution Architecture (IDEA), an approach to planning and execution that provides a unified representational and computational framework for an autonomous agent. The basic hypothesis of IDEA is that a large control system can be structured as a collection of interacting agents, each with the same fundamental structure. We show that planning and real-time response are compatible if the executive minimizes the size of the planning problem. We detail the implementation of this approach on an exploration rover (Gromit, an RWI ATRV Junior at NASA Ames) presenting different IDEA controllers of the same domain and comparing them with more classical approaches. We demonstrate that the approach is scalable to complex coordination of functional modules needed for autonomous navigation and exploration. I.
Probabilistic scan matching for motion estimation in unstructured environments
- in Proceedings of the IEEE/RSJ International conference on Intelligent Robots and Systems (IROS
, 2005
"... Abstract — This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of th ..."
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Cited by 20 (4 self)
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Abstract — This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence. I.
Monte carlo localization in outdoor terrains using multi-level surface maps
- In Proc. of the International Conference on Field and Service Robotics (FSR
, 2007
"... We propose a novel combination of techniques for robustly estimating the position of a mobile robot in outdoor environments using range data. Our approach applies a particle filter to estimate the full six-dimensional state of the robot and utilizes multilevel surface maps which, in contrast to stan ..."
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Cited by 20 (4 self)
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We propose a novel combination of techniques for robustly estimating the position of a mobile robot in outdoor environments using range data. Our approach applies a particle filter to estimate the full six-dimensional state of the robot and utilizes multilevel surface maps which, in contrast to standard elevation maps, allow the robot to represent vertical structures and multiple levels in the environment. We describe probabilistic motion and sensor models to calculate the proposal distribution and to evaluate the likelihood of observations. We furthermore describe an active localization approach which actively selects the sensor orientation of the 2D laser range scanner to improve the localization results. To efficiently calculate the appropriate orientation we apply a clustering operation on the particles and only evaluate potential orientations based on these clusters. Experimental results obtained with a mobile robot in large-scale outdoor environments indicate that our approach yields robust and accurate position estimates. The experiments also demonstrate that multilevel surface maps lead to a significantly better localization performance than standard elevation maps. They additionally show that further accuracy is obtained from the active sensing approach.
Gaussian process modeling of large scale terrain
- In the proceedings of the International Conference on Robotics and Automation (ICRA
, 2009
"... Building a model of large scale terrain that can adequately handle uncertainty and incompleteness in a statistically sound way is a challenging problem. This work proposes the use of Gaussian processes as models of large scale terrain. The proposed model naturally provides a multi-resolution represe ..."
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Cited by 20 (8 self)
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Building a model of large scale terrain that can adequately handle uncertainty and incompleteness in a statistically sound way is a challenging problem. This work proposes the use of Gaussian processes as models of large scale terrain. The proposed model naturally provides a multi-resolution representation of space, incorporates and handles uncertainties aptly and copes with incompleteness of sensory information. Gaussian process regression techniques are applied to estimate and interpolate (to fill gaps in occluded areas) elevation information across the field. The estimates obtained are the best linear unbiased estimates for the data under consideration. A single non-stationary (neural network) Gaussian process is shown to be powerful enough to model large and complex terrain, effectively handling issues relating to discontinuous data. A local approximation method based on a “moving window ” methodology and implemented using KD-Trees is also proposed. This enables the approach to handle extremely large datasets, thereby completely addressing its scalability issues. Experiments are performed on large scale datasets taken from real mining applications. These datasets include sparse mine planning data, which is representative of a GPS based survey, as well as dense laser scanner data taken at different mine-sites. Further, extensive statistical performance evaluation and benchmarking of the technique has been performed through cross validation experiments. They conclude that for dense and/or flat data, the proposed approach will perform very competitively with grid based approaches using standard interpolation techniques and triangulated irregular networks using triangle based interpolation techniques; for sparse and/or complex data however, it would significantly outperform them. 1
Visual motion estimation and terrain modeling for planetary rovers
- International Symposium on Artificial Intelligence for Robotics and Automation in Space (iSARIAS
, 2005
"... The next round of planetary missions will require increased autonomy to enable exploration rovers to travel great distances with limited aid from a human operator. For autonomous operations at this scale, localization and terrain modeling become key aspects of onboard rover functionality. Previous M ..."
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Cited by 17 (4 self)
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The next round of planetary missions will require increased autonomy to enable exploration rovers to travel great distances with limited aid from a human operator. For autonomous operations at this scale, localization and terrain modeling become key aspects of onboard rover functionality. Previous Mars rover missions have relied on odometric sensors such as wheel encoders and inertial measurement units/gyros for on-board motion estimation. While these offer a simple solution, they are prone to wheel-slip in loose soil and drift of biases, respectively. Alternatively, the use of visual landmarks observed by stereo cameras to localize a rover offers a more robust solution but at the cost of increased complexity. Additionally rovers will need to create photo-realistic three-dimensional models of visited sites for autonomous operations on-site and mission planning on Earth. 1.
State Space Sampling of Feasible Motions for High Performance Mobile Robot Navigation in Complex Environments
- Journal of Field Robotics
, 2008
"... Sampling in the space of controls or actions is a well-established method for ensuring feasible local motion plans. However, as mobile robots advance in performance and competence in complex environments, this classical motion-planning technique ceases to be effective. When environmental constraints ..."
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Cited by 16 (6 self)
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Sampling in the space of controls or actions is a well-established method for ensuring feasible local motion plans. However, as mobile robots advance in performance and competence in complex environments, this classical motion-planning technique ceases to be effective. When environmental constraints severely limit the space of acceptable motions or when global motion planning expresses strong preferences, a state space sampling strategy is more effective. Although this has been evident for some time, the practical question is how to achieve it while also satisfying the severe constraints of vehicle dynamic feasibility. The paper presents an effective algorithm for state space sampling utilizing a model-based trajectory generation approach. This method enables high-speed navigation in highly constrained and/or partially known environments such as trails, roadways, and dense off-road obstacle fields. C ○ 2008 Wiley Periodicals, Inc. 1.
