マルチタスク不確実性推定を活用した疎HDマップでのロバスト単眼自己位置推定
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation
セマンティックセグメンテーションと物体検出の不確実性を推定し、微分可能なコストマップとスライディングウィンドウポーズグラフで疎なHDマップ上での単眼6D自己位置推定を高精度・ロバストに実現した。
著者: Kürsat Petek, Kshitij Sirohi, Daniel Büscher, Wolfram Burgard
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research. We present a novel monocular localization approach based on a sliding-window pose graph that leverages predicted uncertainties for increased precision and robustness against challenging scenarios and per frame failures. To this end, we propose an efficient multi-task uncertainty-aware perception module, which covers semantic segmentation, as well as bounding box detection, to enable the localization of vehicles in sparse maps, containing only lane borders and traffic lights. Further, we design differentiable cost maps that are directly generated from the estimated uncertainties. This opens up the possibility to minimize the reprojection loss of amorphous map elements in an association free and uncertainty-aware manner. Extensive evaluation on the Lyft 5 dataset shows that, despite the sparsity of the map, our approach enables robust and accurate 6D localization in challenging urban scenarios
