Real-time navigation in 3D environments based on depth camera data

Abstract

In this paper, we present an integrated approach for robot localization, obstacle mapping, and path planning in 3D environments based on data of an onboard consumer-level depth camera. We rely on state-of-the-art techniques for environment modeling and localization, which we extend for depth camera data. We thoroughly evaluated our system with a Nao humanoid equipped with an Asus Xtion Pro Live depth camera on top of the humanoid's head and present navigation experiments in a multi-level environment containing static and non-static obstacles. Our approach performs in real-time, maintains a 3D environment representation, and estimates the robot's pose in 6D. As our results demonstrate, the depth camera is well-suited for robust localization and reliable obstacle avoidance in complex indoor environments.