MinkUNeXt-SI: 球面座標とLiDAR強度を活用した点群ベース場所認識の改善
MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity
LiDAR点群に球面座標と正規化強度を前処理として加え、Minkowski畳み込みとU-Netを組み合わせた深層学習で場所認識を行い、季節・天候変化に頑健な記述子を生成する手法を提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Judith Vilella-Cantos, Juan José Cabrera, Luis Payá, Mónica Ballesta, David Valiente
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
In autonomous navigation systems, the solution of the place recognition problem is crucial for their safe functioning. But this is not a trivial solution, since it must be accurate regardless of any changes in the scene, such as seasonal changes and different weather conditions, and it must be generalizable to other environments. This paper presents our method, MinkUNeXt-SI, which, starting from a LiDAR point cloud, preprocesses the input data to obtain its spherical coordinates and intensity values normalized within a range of 0 to 1 for each point, and it produces a robust place recognition descriptor. To that end, a deep learning approach that combines Minkowski convolutions and a U-net architecture with skip connections is used. The results of MinkUNeXt-SI demonstrate that this method reaches and surpasses state-of-the-art performance while it also generalizes satisfactorily to other datasets. Additionally, we showcase the capture of a custom dataset and its use in evaluating our solution, which also achieves outstanding results. Both the code of our solution and the runs of our dataset are publicly available for reproducibility purposes.
