Bai Chengchao, Guo Jifeng
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2019 Jun 13;19(12):2681. doi: 10.3390/s19122681.
Accurate perception of the detected terrain is a precondition for the planetary rover to perform its own mission. However, terrain measurement based on vision and LIDAR is subject to environmental changes such as strong illumination and dust storms. In this paper, considering the influence of uncertainty in the detection process, a vibration/gyro coupled terrain estimation method based on multipoint ranging information is proposed. The terrain update model is derived by analyzing the measurement uncertainty and motion uncertainty. Combined with Clearpath Jackal unmanned vehicle-the terrain mapping accuracy test based on ROS (Robot Operating System) simulation environment-indoor Optitrack auxiliary environment and outdoor soil environment was completed. The results show that the proposed algorithm has high reconstruction ability for a given scale terrain. The reconstruction accuracy in the above test environments is within 1 cm, 2 cm, and 6 cm, respectively.
准确感知探测到的地形是行星漫游车执行自身任务的前提条件。然而,基于视觉和激光雷达的地形测量会受到强光和沙尘暴等环境变化的影响。本文考虑探测过程中的不确定性影响,提出了一种基于多点测距信息的振动/陀螺仪耦合地形估计方法。通过分析测量不确定性和运动不确定性,推导了地形更新模型。结合Clearpath Jackal无人驾驶车辆,在基于ROS(机器人操作系统)的模拟环境、室内Optitrack辅助环境和室外土壤环境下完成了地形测绘精度测试。结果表明,所提算法对给定尺度的地形具有较高的重建能力。在上述测试环境中的重建精度分别在1厘米、2厘米和6厘米以内。