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1.	Burgard, W., et al. (author) A Comparison of SLAM Algorithms Based on a Graph of Relations 2009 In: <em>IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)</em>. - : IEEE conference proceedings. ; , s. 2089-2095 Conference paper (peer-reviewed)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.

Burgard, W., et al. (author)
A Comparison of SLAM Algorithms Based on a Graph of Relations
2009
In: <em>IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)</em>. - : IEEE conference proceedings. ; , s. 2089-2095
Conference paper (peer-reviewed)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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