MLOD: 自律走行のためのマルチLiDAR 3D物体検出における外部摂動の認識
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
マルチLiDARの外部キャリブレーション誤差が3D物体検出に与える影響を解析し、各点に不確実性事前分布を伝播させて2段階で融合・補正する検出器MLODを提案した。
著者: Jianhao Jiao, Peng Yun, Lei Tai, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
Extrinsic perturbation always exists in multiple sensors. In this paper, we focus on the extrinsic uncertainty in multi-LiDAR systems for 3D object detection. We first analyze the influence of extrinsic perturbation on geometric tasks with two basic examples. To minimize the detrimental effect of extrinsic perturbation, we propagate an uncertainty prior on each point of input point clouds, and use this information to boost an approach for 3D geometric tasks. Then we extend our findings to propose a multi-LiDAR 3D object detector called MLOD. MLOD is a two-stage network where the multi-LiDAR information is fused through various schemes in stage one, and the extrinsic perturbation is handled in stage two. We conduct extensive experiments on a real-world dataset, and demonstrate both the accuracy and robustness improvement of MLOD. The code, data and supplementary materials are available at: https://ram-lab.com/file/site/mlod
