Progress and Prospects of Polarized Skylight Fused Visual SLAM

doi:10.12382/bgxb.2022.0288

Abstract

Abstract:

The polarized light navigation that imitates the mechanism of polarized light perception of compound eyes is a newly developed technology, which is stable, passive and totally autonomous. The miniaturization and integration of polarization sensors is currently enabling its fusion with low-cost cameras, inertial measurement units (IMU), etc. The solutions for multi-sensor-based simultaneous localization and mapping (SLAM) are firstly reviewed and analyzed. The research progress in polarization navigation sensors and skylight polarization modes are summarized. Then, the polarized light-aided visual-inertial navigation system (VINS) is explored, and this solution is proved to be feasible for outdoor long-term visual SLAM. The challenging problems of polarization imager model that provides directional observation constraint for the whole navigation system are pointed out in terms of outdoor robot operation system (ROS) and graph optimization for reference. The pipeline design of polarized light-aided VINS-Fusion has the technical characteristic of expanding the absolute pose constraint edges of the graph, and further enables the globally consistent navigation trajectory tracking, providing innovative technical supports for autonomous mobile robot navigation and positioning in remote sensing, mapping and military exploration tasks.

Key words: visual SLAM, polarized light navigation, skylight polarization mode, multi-sensor fusion, graph optimization

XIA Linlin, ZHANG Jingjing, CHU Yan, ZHANG Daochang, SONG Ziwei, CUI Jiashuo, LIU Ruimin. Progress and Prospects of Polarized Skylight Fused Visual SLAM[J]. Acta Armamentarii, 2023, 44(6): 1588-1601.

Figures/Tables 10

References 100

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模型	年份	模型名称	模型描述	来源文献
	1995	Rayleigh散射模型	描述晴朗天气下偏振模式的总体特征;但仅考虑了单次散射,且无法描述天空中的中性点对天空偏振模式的影响	Bucholtz^[65]
	2004	Hannay多次散射模型	结合了大气传输模型;将单粒子Rayleigh传输过程推广至多次散射序列	Hannay^[66]
基于Rayleigh散射提出的解析模型	2013	多次散射下大气偏振模式解析模型	从Rayleigh散射模型出发,结合Perez光强分布,考虑大气粒子多次散射特性;构建多次散射下天空偏振光解析模型	吴良海等^[67]
	2014	月光偏振模式解析模型	基于Rayleigh散射,将单个粒子的单次散射从二维空间拓展到三维空间,建立月光偏振模式解析模型;可用于晴朗天气下满月夜间的导航	崔岩等^[68]
	2020	波动水面下偏振模式解析模型	利用Cox-Munk海浪模型描述波浪水面,建立基于Rayleigh散射、波动水面的折射及水面分子散射的波动水面下偏振模式解析模型	褚金奎等^[69]
	2021	等偏振度解析模型	基于Rayleigh散射理论,提取偏振度的分布形态特征,建立大气偏振模式的等偏振度特征模型;实测情况下较Rayleigh散射模型更加准确	汪先球^[70]
	1968	Monte Carlo随机传输模型	面向混浊大气下、经多重散射引起的天空偏振光模式不对称问题;模型复杂度极高,难以用于实时导航	Kattawar等^[71]
	2004	Berry奇异值模型	对偏振中性点进行描述,分别为Arago,Brewster,Babinet和第四中性点	Berry等^[72]
基于Mie散射提出的解析模型	2011	矢量辐射传输模型	基于Monte Carlo法,将矢量传输方程用于偏振模式分布与物理因素相关性研究;更适用于气溶胶、云团等复杂大气环境	Buras等^[73]
	2013	基于RT3的大气偏振模型	基于RT3传输模型,对混浊大气偏振模式进行分析;但对于全天域范围内的混浊大气偏振模式表征仍不适用	王威等^[74]
	2014	混浊大气偏振模式解析模型	以三维天球大气模型为基础,用Monte Carlo法模拟太阳光在大气中的传输过程,实现对混浊大气偏振全局分布的建模	王子谦等^[75]

模型	年份	模型名称	模型描述	来源文献
	1995	Rayleigh散射模型	描述晴朗天气下偏振模式的总体特征;但仅考虑了单次散射,且无法描述天空中的中性点对天空偏振模式的影响	Bucholtz^[65]
	2004	Hannay多次散射模型	结合了大气传输模型;将单粒子Rayleigh传输过程推广至多次散射序列	Hannay^[66]
基于Rayleigh散射提出的解析模型	2013	多次散射下大气偏振模式解析模型	从Rayleigh散射模型出发,结合Perez光强分布,考虑大气粒子多次散射特性;构建多次散射下天空偏振光解析模型	吴良海等^[67]
	2014	月光偏振模式解析模型	基于Rayleigh散射,将单个粒子的单次散射从二维空间拓展到三维空间,建立月光偏振模式解析模型;可用于晴朗天气下满月夜间的导航	崔岩等^[68]
	2020	波动水面下偏振模式解析模型	利用Cox-Munk海浪模型描述波浪水面,建立基于Rayleigh散射、波动水面的折射及水面分子散射的波动水面下偏振模式解析模型	褚金奎等^[69]
	2021	等偏振度解析模型	基于Rayleigh散射理论,提取偏振度的分布形态特征,建立大气偏振模式的等偏振度特征模型;实测情况下较Rayleigh散射模型更加准确	汪先球^[70]
	1968	Monte Carlo随机传输模型	面向混浊大气下、经多重散射引起的天空偏振光模式不对称问题;模型复杂度极高,难以用于实时导航	Kattawar等^[71]
	2004	Berry奇异值模型	对偏振中性点进行描述,分别为Arago,Brewster,Babinet和第四中性点	Berry等^[72]
基于Mie散射提出的解析模型	2011	矢量辐射传输模型	基于Monte Carlo法,将矢量传输方程用于偏振模式分布与物理因素相关性研究;更适用于气溶胶、云团等复杂大气环境	Buras等^[73]
	2013	基于RT3的大气偏振模型	基于RT3传输模型,对混浊大气偏振模式进行分析;但对于全天域范围内的混浊大气偏振模式表征仍不适用	王威等^[74]
	2014	混浊大气偏振模式解析模型	以三维天球大气模型为基础,用Monte Carlo法模拟太阳光在大气中的传输过程,实现对混浊大气偏振全局分布的建模	王子谦等^[75]

类别	年份	相机类型	融合策略	模型描述	载体	位置定位精度	来源文献
偏振罗盘输出	2016	双目	EKF	MEMS IMU遵循惯性解算;偏振罗盘直接输出载体偏航信息;单目VO提供连续帧位姿观测	车辆	3D RMSE为4.3m(道路环境)	Kong等^[82]
	2016	双目	IEKF+拓扑图节点递推	偏振定向辅助远/近特征点的双目视觉/微惯性组合IEKF;以图节点导航信息扩展&场景识别实现图节点递推	车辆	3D RMSE<12.0m(道路环境)	Xian等^[83]
	2017	单目	EKF	偏振罗盘集成至VINS;IMU遵循惯性解算,单目VO提供相对位姿;基于IMU输出的姿态,偏振提供平台航向	车辆	RMSE为25.9m(行驶距离的1.03%)	Wang等^[84]
偏振成像观测	2018	单目	Kalman滤波	IMU遵循惯性解算;偏振光传感器提供定向约束;单目相机提供几何地图,并为整机提供位置约束	车辆	RMSE为2.04m(行驶距离的0.01%)	Fan等^[85]
	2021	单目	AKF	像素化偏振视觉与VINS集成;利用偏振成像和VINS水平姿态角进行偏振定向解算	无人车	RMSE为0.64m (校园环境)	Zhou等^[86]
	2022	双目	图优化	将基于Berry模型的偏振观测集成至VINS-Fusion;以偏振角作为图模型节点的新增属性,以航向误差编码约束边	Bulldog-CX 机器人	RMSE<0.28m (校园环境)	Xia等^[87]

类别	年份	相机类型	融合策略	模型描述	载体	位置定位精度	来源文献
偏振罗盘输出	2016	双目	EKF	MEMS IMU遵循惯性解算;偏振罗盘直接输出载体偏航信息;单目VO提供连续帧位姿观测	车辆	3D RMSE为4.3m(道路环境)	Kong等^[82]
	2016	双目	IEKF+拓扑图节点递推	偏振定向辅助远/近特征点的双目视觉/微惯性组合IEKF;以图节点导航信息扩展&场景识别实现图节点递推	车辆	3D RMSE<12.0m(道路环境)	Xian等^[83]
	2017	单目	EKF	偏振罗盘集成至VINS;IMU遵循惯性解算,单目VO提供相对位姿;基于IMU输出的姿态,偏振提供平台航向	车辆	RMSE为25.9m(行驶距离的1.03%)	Wang等^[84]
偏振成像观测	2018	单目	Kalman滤波	IMU遵循惯性解算;偏振光传感器提供定向约束;单目相机提供几何地图,并为整机提供位置约束	车辆	RMSE为2.04m(行驶距离的0.01%)	Fan等^[85]
	2021	单目	AKF	像素化偏振视觉与VINS集成;利用偏振成像和VINS水平姿态角进行偏振定向解算	无人车	RMSE为0.64m (校园环境)	Zhou等^[86]
	2022	双目	图优化	将基于Berry模型的偏振观测集成至VINS-Fusion;以偏振角作为图模型节点的新增属性,以航向误差编码约束边	Bulldog-CX 机器人	RMSE<0.28m (校园环境)	Xia等^[87]

类别	年份	传感器类型	融合策略	载体	来源文献
	2020	偏振传感器+激光雷达+轮式里程计	EKF/UKF/PF	Kobuki机器人	Du等^[91]
偏振罗盘输出	2020	偏振传感器+SINS	Kalman滤波	水平基座	Du等^[92]
	2020	偏振传感器+MEMS IMU+GPS	EKF	四旋翼飞行器	褚金奎等^[93]
	2021	偏振传感器+双目相机	图优化	车辆	褚金奎等^[94]
	2020	偏振光+INS+光流传感器	UKF	六足机器人	曾云豪等^[95]
	2020	偏振光+MEMS IMU	EKF	车辆	范晨等^[96]
偏振成像观测	2019	偏振光+MEMS IMU+地磁	Kalman滤波	车辆	He等^[97]
	2019	偏振光+MEMS IMU+GNSS	Kalman滤波	车辆	He等^[98]
	2020	偏振光+MEMS IMU+地磁+GNSS	Sage-Husa Kalman滤波	车辆	Yuan等^[99]
	2022	偏振光+SINS+地磁+BDS	联邦Kalman滤波	车辆	马伟等^[100]