Scan Context++: 都市環境における回転・横方向変動に頑健な構造的場所認識
Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments
LiDARの距離データから構造的外観に基づいて場所を認識する記述子を提案し、回転と並進の両方に頑健にした上で、トポロジカルな場所検索と1自由度の半メトリック定位を橋渡しする手法を開発した。
著者: Giseop Kim, Sunwook Choi, Ayoung Kim
分類: cs.RO
原文アブストラクト
Place recognition is a key module in robotic navigation. The existing line of studies mostly focuses on visual place recognition to recognize previously visited places solely based on their appearance. In this paper, we address structural place recognition by recognizing a place based on structural appearance, namely from range sensors. Extending our previous work on a rotation invariant spatial descriptor, the proposed descriptor completes a generic descriptor robust to both rotation (heading) and translation when roll-pitch motions are not severe. We introduce two sub-descriptors and enable topological place retrieval followed by the 1-DOF semi-metric localization thereby bridging the gap between topological place retrieval and metric localization. The proposed method has been evaluated thoroughly in terms of environmental complexity and scale. The source code is available and can easily be integrated into existing LiDAR simultaneous localization and mapping (SLAM).
関連論文
- 全方位・長期場所認識のためのマルチセンサベンチマークデータセットと解析SLAM/場所認識
