面向火星自主漫游任务的视觉-惯性感知系统

Vision-Inertial Perception System for Autonomous Rovers

精确的位姿估计以及对周围环境中的障碍物的实时感知,是探测器在火星表面进行自主漫游的基础.然而,火星探测器受到自身质量、体积和能源供应等因素的影响,计算资源和设备功率受到严格限制,这给感知系统的设计与实现提出了挑战.本文针对火星探测器计算资源严重受限的问题,设计了一种基于视觉-惯性多传感器滤波融合的智能感知系统,其主要包括两个模块:1)基于多状态约束卡尔曼滤波MSCKF(multi state constrained Kalman filter)算法的视觉-惯性里程估计模块,实现了相对误差小于1.5%的较高定位精度;2)使用GPU加速的高程地图构建算法,实现了稠密地形图的实时构建.其中,高程地图与机器人位姿采用概率最优的方式进行融合,保证了感知系统整体的概率一致性.相比于现有感知系统,本文所提出的方法可以仅使用双目视觉和IMU实现机器人的位姿估计和周围环境高程地图的构建,并通过合理的算法设计、GPU硬件加速等方法,最终整套系统在30W板载系统上实现了位姿估计输出频率400Hz、地图输出频率4.2Hz的实时运行效果,充分验证了本系统的轻量化特性,能够有效应用于火星车的自主探测任务.

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.