Polarized light-aided VINS: Global heading measurements and graph optimization based multi-sensor fusion

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.

An Improved Initialization Method for Monocular Visual-Inertial SLAM

In the aim of improving the positioning accuracy of the monocular visual-inertial simultaneous localization and mapping (VI-SLAM) system, an improved initialization method with faster convergence is proposed. This approach is classified into three parts: Firstly, in the initial stage, the pure vision measurement model of ORB-SLAM is employed to make all the variables visible. Secondly, the frequency of the IMU and camera was aligned by IMU pre-integration technology. Thirdly, an improved iterative method is put forward for estimating the initial parameters of IMU faster. The estimation of IMU initial parameters is divided into several simpler sub-problems, containing direction refinement gravity estimation, gyroscope deviation estimation, accelerometer bias, and scale estimation. The experimental results on the self-built robot platform show that our method can up-regulate the initialization convergence speed, simultaneously improve the positioning accuracy of the entire VI-SLAM system.

Study of Indoor Visual SLAM System for Semi-autonomous Robot Platform

This study propose the use of heterogeneous visual landmarks, points and line segments, to achieve effective cooperation in indoor SLAM environments. In order to achieve un-delayed initialization required by the bearing-only observations, the well-known inverse-depth parameterization is adopted to estimate 3D points. Similarly, to estimate 3D line segments, we present a novel parameterization based on anchored Plücker coordinates, to which extensible endpoints are added

An Improved Initialization Method for Monocular Visual-Inertial SLAM

In the aim of improving the positioning accuracy of monocular visual inertial simultaneous localization and mapping (VI-SLAM) system, an improved initialization method with faster convergence is proposed. This approach is classified as three parts: Firstly, in the initial stage, the pure vision measurement model of ORB-SLAM is employed to make all the variables visible. Secondly, the frequency of IMU camera was aligned by IMU preintegration technology. Thirdly, an improved iterative method is put forward for estimating the initial parameters of IMU faster. The estimation of IMU initial parameters is divided into several simpler sub-problems, containing direction refinement gravity estimation, gyroscope deviation estimation, accelerometer bias and scale estimation. The experimental results on the self-built robot platform show that our method can up-regulate the initialization convergence speed, simultaneously improve the positioning accuracy of the entire VI-SLAM system.