Introduction
Recognizing previously observed locations is a significant challenge in the areas of autonomous mapping and navigation. The place recognition problem arises in applications ranging from pose initialization and localization using prior maps, closing loops and relocalizing lost robots in simultaneous localization and mapping (SLAM), and merging maps collected at different times or by multiple robots. In this paper, we present and evaluate a general approach for efficiently recognizing and identifying previously observed areas. While we focus on two-dimensional laser point cloud maps, the methodology described is applicable to other sensing modalities and map representations. The maps we consider are constructed from large datasets taken over tens to hundreds of kilometers under regular driving conditions and road speeds in urban and suburban environments. At this scale, efficiency—especially in the case of online SLAM, which must be solved in real-time—is critical.
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Zlot, R., Bosse, M. (2009). Place Recognition Using Keypoint Similarities in 2D Lidar Maps. In: Khatib, O., Kumar, V., Pappas, G.J. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00196-3_42
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DOI: https://doi.org/10.1007/978-3-642-00196-3_42
Publisher Name: Springer, Berlin, Heidelberg
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