4足歩行ロボットのための深度カメラによる3次元環境認識と自己位置推定

抄録

SLAM based on map generation of the surrounding environment and self-position estimation is effective for multi-legged robots to demonstrate its performance. Most of the previous SLAM researches focused on odometry using wheels, and there are few examples of verification using a multi-legged robot. In addition, due to the limited payload of a multi-legged robot, it is necessary to perform processing with limited sensors and onboard resources. In this study, a SLAM method using a small quadruped robot, a depth camera that can be mounted on the robot, and its built-in IMU sensor is proposed. A method to estimate the position and posture of the robot by mapping feature points obtained from color images between image frames and distance images is implemented, and evaluated.