关键词：光流法；移动机器人；相机运动；运动补偿
摘 要：A legged crawler's unsteady dynamics are explored with an emphasis on how these affect optical flow estimation, which mediates navigation. The optical flow algorithm's gains are further tuned using policy gradient reinforcement learning so as to improve motion estimation for specific unsteady regimes. This approach is demonstrated in an obstacle avoidance scenario.