スマートエッジセンサとの協調知覚のための外部カメラによる移動ロボット姿勢推定
External Camera-based Mobile Robot Pose Estimation for Collaborative Perception with Smart Edge Sensors
複数の固定カメラのRGB画像から深層学習でロボットの2Dキーポイントを検出し、多視点誤差最小化によりロボットの姿勢を推定する手法を提案。ロボットの内部ナビゲーションと比較して3cm・1度以下の精度でドリフトなく追従でき、移動ロボットと固定センサの観測を融合して240m²の屋内環境の3Dセマンティックマップを構築する実応用を示した。
著者: Simon Bultmann, Raphael Memmesheimer, Sven Behnke
分類: cs.RO, cs.CV
原文アブストラクト
We present an approach for estimating a mobile robot's pose w.r.t. the allocentric coordinates of a network of static cameras using multi-view RGB images. The images are processed online, locally on smart edge sensors by deep neural networks to detect the robot and estimate 2D keypoints defined at distinctive positions of the 3D robot model. Robot keypoint detections are synchronized and fused on a central backend, where the robot's pose is estimated via multi-view minimization of reprojection errors. Through the pose estimation from external cameras, the robot's localization can be initialized in an allocentric map from a completely unknown state (kidnapped robot problem) and robustly tracked over time. We conduct a series of experiments evaluating the accuracy and robustness of the camera-based pose estimation compared to the robot's internal navigation stack, showing that our camera-based method achieves pose errors below 3 cm and 1{\deg} and does not drift over time, as the robot is localized allocentrically. With the robot's pose precisely estimated, its observations can be fused into the allocentric scene model. We show a real-world application, where observations from mobile robot and static smart edge sensors are fused to collaboratively build a 3D semantic map of a $\sim$240 m$^2$ indoor environment.
