An Inertial Odometry and Enhanced Occupancy Grid Inertial SLAM for Legged Robots

Abstract

Accurate position estimation is crucial for legged robots, the inertial odometry based on inertial measurement units (IMUs) is low-cost and easy to deploy, and inertial simultaneous localization and mapping (SLAM) methods can maintain stable performance on different robot platforms and degraded environments. In this paper, a single IMU is mounted on the robot foot to construct the inertial odometry without relying on kinematic modeling. The stance phase and optimal zero-velocity point (OZP) are detected through IMU outputs, and the once zero-velocity update is performed at each OZP to correct the drift of inertial odometry. Furthermore, the enhanced occupancy grid inertial SLAM framework is introduced. In the grid map update section, the motion control vectors derived from the inertial odometry are used with the Bresenham algorithm to calculate accessibility probabilities of grid cells, which addresses detail loss from grid map discretization and the imbalance in the accessibility probability resulting from repeated erroneous map updates. Experimental tests on the Unitree B1 robot demonstrate that our system provides further improvement in position estimation.