Real-time localization, mapping and navigation for quadruped robots

Abstract

Autonomous quadruped robots require localization and mapping for navigation. Different from mobile robots, quadruped robots need local dense maps with more detailed information for motion planning. A key challenge, limited by the capacity of computers on robots, the perception systems have to balance the properties of the speed and accuracy to ensure that robots reach their destinations quickly and safely. In this paper, we propose a complete solution for the autonomous movement of quadruped robots. Localization is achieved by the Extended Kalman Filter which is updated by the results from laser based the ICP algorithm. Dense maps are created by raw ranger sensor data and accumulated base on accurate localization results. And then, dense maps are transformed to the occupancy grid maps which contain free and occupied space information for planning. For implementations, we demonstrate the effectiveness of our approach in a complex outdoor environment.