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- Burgard, W., et al.
(författare)
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A Comparison of SLAM Algorithms Based on a Graph of Relations
- 2009
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Ingår i: <em>IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)</em>. - : IEEE conference proceedings. ; , s. 2089-2095
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Konferensbidrag (refereegranskat)abstract
- In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches, namely to consider the energy that is needed to deform the trajectory estimated by a SLAM approach into the ground truth trajectory. Our method enables 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 SLAM 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 an easy analysis and objective comparisons between different SLAM approaches.
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- Kümmerle, R., et al.
(författare)
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Large Scale Graph-based SLAM using Aerial Images as Prior Information
- 2009
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Ingår i: Proceedings of Robotics Science and Systems (RSS). - : MIT Press.
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Konferensbidrag (refereegranskat)abstract
- To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.
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- Kümmerle, R., et al.
(författare)
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Large Scale Graph-based SLAM using Aerial Images as Prior Information
- 2011
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Ingår i: Autonomous Robots. - : Springer. - 0929-5593 .- 1573-7527. ; 30:1, s. 25-39
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Tidskriftsartikel (refereegranskat)abstract
- The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.
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- Kümmerle, R., et al.
(författare)
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On Measuring the Accuracy of SLAM Algorithms
- 2009
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Ingår i: Autonomous Robots. - : Springer. - 0929-5593 .- 1573-7527. ; 27:4, s. 387-407
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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