Gaussian process-driven monocular vision-based state estimation for autonomous navigation

A monocular vision-based pose estimation framework is developed for autonomous real-time navigation in dynamic environments without using the global navigation satellite system (GNSS). The framework models distances from landmarks and static objects as a function of object segmentation information obtained from monocular visual inputs using Gaussian process regression (GPR), and utilizes a nonlinear optimization and an optimal variance filter with covariance adaptation to estimate the mobile robot/vehicle pose, subject to various initial state and error covariance conditions (even without intermitted updates from GNSS). The stability of the developed state observer and the boundedness of the estimation error covariance are also investigated. The real-time performance of the designed framework is experimentally evaluated using a vehicle test platform in various dynamic environments and under perceptually degraded conditions, which include intermittent visual inputs and different navigational scenarios. Consistent and accurate pose estimation, bounded estimation error, and computational efficiency of the proposed method are confirmed by comprehensive tests in highly-dynamic urban settings. The maximum processing time for the state estimator is less than 10 ms, making the framework computationally efficient for any visual input stream lower than 30-50 Hz. Furthermore, a visual perception system to customize essential map elements, including static landmarks and road features, are provided and experimentally evaluated in busy downtown and university campus areas.