为探究环境感知设备在SLAM算法应用过程中的光照适应性问题,在不同光照强度下分别进行激光雷达和深度相机SLAM算法的验证性评估实验.基于四轮差速机器人,搭载 16 线激光雷达和深度相机,结合LOAM(Lidar Odometry And Mapping)和 RTAB-MAP(Real-Time Ap-pearance-Based Mapping)算法,分别在明暗环境中分析验证设备光照适应性.实验结果表明:在明亮环境下,基于视觉SLAM和激光 SLAM 系统偏差的中误差分别为 0.203 和 0.644 m;在黑暗环境中两者偏差的中误差分别为 0.282 和0.683 m;深度相机在明、暗环境中的定位建图效果均优于激光雷达,深度相机的光照适应性更强.
Illumination adaptability of SLAM applications in real scenes
To explore the illumination adaptability of environmental perception equipment in application of SLAM(Simultaneous Localization And Mapping),comparative experiments of lidar and depth camera for SLAM were car-ried out under different illumination intensities.Combined with the LOAM(Lidar Odometry And Mapping)and RT-AB-MAP(Real-Time Appearance-Based Mapping)algorithms,a 16-line lidar and a depth camera were placed on a four-wheel differential robot to carry out SLAM application in bright and dark environments.The experimental results show that in bright environment,the median errors of system deviations are 0.203 m and 0.644 m for the visual SLAM and lidar SLAM,respectively,which are 0.282 m and 0.683 m respectively in dark environment.The depth camera outperforms the lidar in positioning and mapping performance in both bright and dark environment,and it can be concluded that the depth camera is more illumination adaptable.
lidar SLAMvisual SLAM(VSLAM)real-time appearance-based mapping(RTAB-MAP)lidar odometry and mapping(LOAM)
国家科技期刊平台
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