Integrated perception, mapping, and footstep planning for humanoid navigation among 3d obstacles

Abstract — In this paper, we present an integrated navigation system that allows humanoid robots to autonomously navigate in unknown, cluttered environments. From the data of an on-board consumer-grade depth camera, our system estimates the robot’s pose to compensate for drift of odometry and maintains a heightmap representation of the environment. Based on this model, our system iteratively computes sequences of safe actions including footsteps and whole-body motions, leading the robot to target locations. Hereby, the planner chooses from a set of actions that consists of planar footsteps, step-over actions, as well as parameterized step-onto and step-down actions. To efficiently check for collisions during planning, we developed a new approach that takes into account the shape of the robot and the obstacles. As we demonstrate in experiments with a Nao humanoid, our system leads to robust navigation in cluttered environments and the robot is able to traverse highly challenging passages. I