Name
Abstract | ||
|---|---|---|
The recognition of places that have already been visited is a fundamental requirement for a mobile robot. This particularly concerns the detection of loop closures while mapping environments as well as the global localization w.r.t. to a prior map. This paper introduces a novel solution to place recognition with 2D LIDAR scans. Existing approaches utilize descriptors covering the local appearance of discriminative features within a bag-of-words (BOW) framework accompanied with approximate geometric verification. Though limiting the set of potential matches their performance crucially drops for increasing number of scans making them less appropriate for large scale environments. We present Geometrical Landmark Relations (GLARE), which transform 2D laser scans into pose invariant histogram representations. Potential matches are found in sub-linear time using an efficient Approximate Nearest Neighbour (ANN) search. Experimental results obtained from publicly available datasets demonstrate that GLARE significantly outperforms state-of-the-art approaches in place recognition for large scale outdoor environments, while achieving similar results for indoor settings. Our Approach achieves recognition rates of 93% recall at 99% precision for a dataset covering a total path of about 6.5 km. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IROS.2014.6943277 | Intelligent Robots and Systems |
Keywords | DocType | ISSN |
feature extraction,geometry,image recognition,optical radar,optical scanners,2D LIDAR,2D laser scans,approximate geometric verification,approximate nearest neighbour search,bag-of-words framework,discriminative feature,geometrical landmark relations,large scale outdoor environment,large scale place recognition,local appearance,loop closure | Conference | 2153-0858 |
Citations | PageRank | References |
6 | 0.46 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marian Himstedt | 1 | 8 | 1.51 |
| Jan Frost | 2 | 6 | 0.46 |
| Sven Hellbach | 3 | 63 | 9.77 |
| Hans-Joachim Böhme | 4 | 143 | 20.86 |
| Erik Maehle | 5 | 676 | 130.34 |