Visual SLAM Based Spatial Recognition and Visualization Method for Mobile AR Systems

The simultaneous localization and mapping (SLAM) market is growing rapidly with advances in Machine Learning, Drones, and Augmented Reality (AR) technologies. However, due to the absence of an open source-based SLAM library for developing AR content, most SLAM researchers are required to conduct their own research and development to customize SLAM. In this paper, we propose an open source-based Mobile Markerless AR System by building our own pipeline based on Visual SLAM. To implement the Mobile AR System of this paper, we use ORB-SLAM3 and Unity Engine and experiment with running our system in a real environment and confirming it in the Unity Engine’s Mobile Viewer. Through this experimentation, we can verify that the Unity Engine and the SLAM System are tightly integrated and communicate smoothly. In addition, we expect to accelerate the growth of SLAM technology through this research.

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.

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