能動的なマルチカメラ視覚教示・再現のためのカメラ性能モデル学習
Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat
教示走行時のカメラ映像から各カメラの性能モデルを学習し、再現走行時に最も情報量の多いカメラを能動選択することで、遮蔽や環境変化に頑健なマルチカメラ視覚教示・再現を実現した。四足歩行ロボットでの実機実験と4カメラへの拡張も示す。
著者: Matías Mattamala, Milad Ramezani, Marco Camurri, Maurice Fallon
分類: cs.RO
原文アブストラクト
In dynamic and cramped industrial environments, achieving reliable Visual Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust method for non-synchronized multi-camera VT&R. Our contribution are expected Camera Performance Models (CPM) which evaluate the camera streams from the teach step to determine the most informative one for localization during the repeat step. By actively selecting the most suitable camera for localization, we are able to successfully complete missions when one of the cameras is occluded, faces into feature poor locations or if the environment has changed. Furthermore, we explore the specific challenges of achieving VT&R on a dynamic quadruped robot, ANYmal. The camera does not follow a linear path (due to the walking gait and holonomicity) such that precise path-following cannot be achieved. Our experiments feature forward and backward facing stereo cameras showing VT&R performance in cluttered indoor and outdoor scenarios. We compared the trajectories the robot executed during the repeat steps demonstrating typical tracking precision of less than 10cm on average. With a view towards omni-directional localization, we show how the approach generalizes to four cameras in simulation. Video: https://youtu.be/iAY0lyjAnqY
