空中3Dイメージングソナーによる完全6自由度SLAM
RTISSLAM-3D: Full 6-DoF SLAM With an In-Air 3D Imaging Sonar
空中3Dイメージングソナー・IMU・車輪エンコーダを搭載した車輪ロボット向けに、ソナー画像を球面上のCFAR検出で3D点群化し、音響オドメトリ・極座標サブマップ記述子による場所認識・3Dレジストレーションによるループ閉じ込みを統合した6自由度ポーズグラフSLAMを構築した。森林545m走行で水平誤差を1.74mから0.28mに低減し、3Dソナーマップの94.6%がLiDAR地図の0.3m以内に収まる精度を達成した。
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著者: Jan Steckel, Vincent Katter, Wouter Jansen
分類: cs.RO
原文アブストラクト
Echolocating bats navigate dense forests using sonar alone, and in-air 3D imaging sonars now capture the entire frontal hemisphere with every pulse. Sonar SLAM, however, has so far been planar: current systems estimate a 2D trajectory and discard the elevation information in the sonar images, an assumption that does not hold on rough and sloped terrain such as a forest floor. In this paper, we introduce RTISSLAM-3D, a novel 6-DoF SLAM system for a wheeled robot that carries an in-air 3D imaging sonar, an IMU and wheel encoders. A CFAR detector on the sphere reduces every sonar image to a sparse set of 3D detections, which we use for a 3D acoustic odometry that models the shape of the sonar beam, for place recognition with polar submap descriptors, and for the verification of loop closures by 3D registration, all combined in a 6-DoF pose graph with attitude factors. We evaluated RTISSLAM-3D on a 545m drive through a forest, with a 3D lidar SLAM as reference. The loop closures reduced the horizontal error of the acoustic odometry from 1.74m to 0.28m, with a height error of 0.12m, and 94.6% of the resulting 3D sonar map lies within 0.3 m of the lidar map.