Adaptive Vison Aided Integrated Navigation For Dynamic Unknown Environments

Abstract

In this research, a novel method for visual odometry (VO) and the integration with multi-‎sensors navigation systems for vehicular platforms is proposed. The proposed method ‎partitions the field of single camera view into regions of interests where each region likely ‎contains different types of visual features. By applying computer vision processing ‎techniques, ambiguous pose estimation is calculated up to a scale factor. The proposed ‎method uses aiding measurements from vehicle’s odometer to adaptively resolve the scale ‎factor ambiguity problem in monocular camera systems. Unlike some state-of-art ‎approaches, this work does not depend on offline pre-processing or predefined landmarks ‎or visual maps. In addition, this work addresses unknown uncontrolled environments where ‎moving objects likely exist. Innovative odometer-aided Local Bundle Adjustment (LBA) ‎along with a fuzzy C-mean clustering mechanism is proposed to reject outliers ‎corresponding to moving objects. A Gaussian Mixture approach is also applied to detect ‎visual background regions during stationary periods which enables further rejection of ‎moving objects. Finally, an empirical scoring method is applied to calculate a matching ‎score of the different visual features and to use this score in a Kalman filter as ‎measurement covariance noise to integrate VO-estimated pose changes within a larger multi-‎sensors integrated navigation system. Experimental work was performed with a physical ‎vehicular platform equipped by MEMS inertial sensors, GPS, speed measurements and ‎GPS-enabled camera. The experimental work includes three testing vehicular trajectories in ‎downtown Toronto and the surrounding areas. The experimental work showed significant ‎navigation improvements during long GPS outages where only VO is fused with inertial ‎sensors and the vehicle’s speed measurements.‎