Novel INS/GPS/Fisheye-Camera Loosely/Tightly Coupled Enhancing Robust Navigation in Dense Urban Environment

Abstract

This paper addresses an original problem of integrated navigation system INS/GPS in urban environment when LOS/NLOS measurement could be mixed and sequentially available. To solve this problem, multiple Kalman filtering algorithms were investigated and tested on a real designed platform called: nanoiBB, which is an integrated navigation and recording system developed by LASSENA laboratory in ETS-Montreal. With IMU 9DOF and GPS receiver, Loosely and Tightly coupled approach were implemented and compared for long duration navigation in the city of Montreal, in Obstructed and Unobstructed areas, when experimental data collected from four (04) iBB systems were used to analyze and validate loosely/tightly coupled Information Fusion method for INS/GPS integrated system. To achieve that, different scenarios and observability conditions were assumed and then implemented in different Kalman filtering frameworks in post processing; to achieve the best NLOS detection, Fisheye camera view was selected to detect NLOS regions and select the best adaptive or robust nonlinear filters for loosely/tightly integration. It is important to mention that there is no rejection of satellites, instead, adaptive fading factors and Hinfinity versions of Gauss quadrature Kalman filters was designed and applied. During the tests, micro-iBB integrated navigation systems and recorders have demonstrated good performances using EKF/UKF, then with much higher efficiency when using High degree Cubature Kalman filters. It was found that it is a good candidate for driving assessment and data recording systems, in real time and post processing data analysis for event emergency detection even in dense urban environment.