Abstract: Accurate positional estimation and real time perception of obstacles in the surrounding environment are the basis for autonomous roving on the Mars. However, the Mars rover is limited by its own weight, volume, energy supply and other factors. The computational resources and device power are severely constrained, which poses a challenge to the design and implementation of the perception system. In this paper, we design an intelligent perception system based on vision inertial multi sensor filter fusion to address the problem of severely limited computing resources of Mars rovers. The system includes two main modules. A visual inertial odometry estimation module based on MSCKF (multi state constrained Kalman filter) algorithm achieves a high localization accuracy with relative error less than 1.5% An elevation map construction algorithm using GPU acceleration achieves real time construction of dense terrain maps. The elevation map and robot poses are fused in a probability optimal way to ensure the probability consistency of the perception system. Compared with existing perception systems, the proposed method can achieve the robot’s pose estimation and the construction of the elevation map of the surrounding environment only using binocular vision and IMU, and finally achieve 400 Hz poses’ output frequency and 4.2 Hz maps’ output frequency in 30W SoC through the rational algorithm design and GPU hardware acceleration.

Key words: autonomous roaming, multi sensor fusion, state estimation, mapping