エントロピーに基づく2D LiDARと自己運動センサ間のSim(3)キャリブレーション
Entropy-Based $Sim(3)$ Calibration of 2D Lidars to Egomotion Sensors
エントロピーを用いた点群再構成により、2D LiDARと剛結合された単眼カメラ間のスケール未知のSim(3)変換を推定するキャリブレーション手法を提案し、シミュレーションと実機データでミリメートル・サブ度精度を実証した。
著者: Jacob Lambert, Lee Clement, Matthew Giamou, Jonathan Kelly
分類: cs.RO
原文アブストラクト
This paper explores the use of an entropy-based technique for point cloud reconstruction with the goal of calibrating a lidar to a sensor capable of providing egomotion information. We extend recent work in this area to the problem of recovering the $Sim(3)$ transformation between a 2D lidar and a rigidly attached monocular camera, where the scale of the camera trajectory is not known a priori. We demonstrate the robustness of our approach on realistic simulations in multiple environments, as well as on data collected from a hand-held sensor rig. Given a non-degenerate trajectory and a sufficient number of lidar measurements, our calibration procedure achieves millimetre-scale and sub-degree accuracy. Moreover, our method relaxes the need for specific scene geometry, fiducial markers, or overlapping sensor fields of view, which had previously limited similar techniques.
