Polarized light-aided visual-inertial navigation system: global heading measurements and graph optimization-based multi-sensor fusion

Abstract

Polarized skylight is as fundamental a constituent of passive navigation as geomagnetic field. In regards to its applicability to outdoor robot localization, a polarized light-aided VINS (abbreviates ‘visual-inertial navigation system’) modelization dedicated to globally optimized pose estimation and heading correction is constructed. The combined system follows typical visual SLAM (abbreviates ‘simultaneous localization and mapping’) frameworks, and we propose a methodology to fuse global heading measurements with visual and inertial information in a graph optimization based estimator. With ideas of ‘new-added attribute of each vertex and heading error encoded constraint edges’, the heading, as absolute orientation reference, is estimated by Berry polarization model and continuously updated in a graph structure. The formulized graph optimization process for multi-sensor fusion is simultaneously provided. In terms of campus road experiments on Bulldog-CX Robot platform, results are compared against purely stereo camera-dependent and VINS Fusion frameworks, revealing our design is substantially more accurate than others with both locally and globally consistent position and attitude estimates. As essentially passive, anatomically coupled and drifts calibratable navigation mode, the polarized light-aided VINS may therefore be considered as a tool candidate for a class of visual SLAM based multi-sensor fusion.

Passive Polarized Vision for Autonomous Vehicles: A Review

“…As far as we know, only PbCs have been combined with Integrated Navigation Systems (INS). They could be either based on a PbC/INS with a heading accuracy ranging from 0.08° to 0.8° in sunny weather, but 0.8° to 1.5° in cloudy conditions [ 179 , 240 , 241 , 243 , 244 , 245 , 246 ], or PbC/GNSS/INS with a heading accuracy ranging from 0.02° to 6.5° [ 175 , 247 , 248 , 249 ], or PbC/SLAM/INS with a heading accuracy from 0.28° to 4.7° and a geographical position error from 1.96 m to 8.7 m [ 214 , 250 , 251 , 252 , 253 ]. The heading and trajectory accuracy can therefore be improved by about 40% compared to conventional navigation systems in complex outdoor environments [ 22 ].…”

A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features

“…The application of visual compass can be classified into different types based on various criteria, including type of used sensor, method of estimation, and type of used features. Depending on the used sensors, visual compasses can be classified as monocular cameras [1][2][3], stereo cameras [4][5][6], RGB-D camera [7][8][9], polarized light camera [10][11][12], omnidirectional cameras [3,13,14], or a combination of sensors [15,16]. Meanwhile, visual compasses have different estimation methods by focusing on various types of information, such as 2D-3D correspondence [17][18][19], virtual visual servoing (VVS) [20][21][22], holistic method [23][24][25], extended Kalman filter (EKF) [26][27][28], and appearance-based method [3,13,29].…”

Measurement Modeling and Performance Analysis of a Bionic Polarimetric Imaging Navigation Sensor Using Rayleigh Scattering to Generate Scattered Sunlight

“…Polarization is another dimension of light, just like spectrum and intensity, which can provide distinct and useful information about a visual scene [ 1 ] and is applied in many scenarios, such as microscopy imaging [ 2 ], optical precision measurement [ 3 , 4 ], and biological polarization navigation [ 5 , 6 , 7 ]. Many animals, particularly insects, are sensitive to the polarization of light and use this information for navigation, detection, and communication [ 8 ].…”