A vision-based perception framework for outdoor navigation tasks applicable to legged robots

A vision-based perception system for understanding the outdoor environment has been proposed. This system utilizes and applies the useful information from the binocular cameras of a legged robot for landform - terrain - ground environmental perception. The ground texture recognition can provide information about the material of the ground then a legged robot can choose an appropriate walking pose accordingly. With the terrain perception and obstacle detection, combined with semantic segmentation of the environment, a legged robot can move towards the target with intelligent obstacle avoidance strategy. Based on this vision-based perception system, we propose a vision-based perception framework for outdoor navigation tasks applicable to legged robots. In this framework, environmental modeling and situation assessment will be firstly carried out combined with multi-mode sensor fusion, and then footsteps and local path planning can be generated.