Quantitative evaluation and information fusion of road edges for accurate unstructured road tracking

This research focuses on the problem of Autonomous Land Vehicle (ALV) navigating on the unstructured roads which have no lane marks, may have degraded surfaces and edges, and may have shrub and weed alongside. Because of the complexity and irregularity of the road edges, the accurate and stable edges can not be obtained on the pixel level. We developed a novel sensor fusion and motion fusion parallel strategy on the feature level to model the unstructured road edges accurately. The road edges from grey images and lane cues from Laser Range Finders (LRF) are integrated to form a consistent estimation of the edge position based on Covariance Intersection (CI) filter. The fused edges of consecutive frames are associated and integrated also based on CI. The quantitative evaluation method is also proposed to assess the quality of road edges. These algorithms are tested on a variety of complex unstructured roads, the autonomous navigation is successful and the average speed achieves 20km/h. The spot navigation experiments show the effectiveness and efficiency of these algorithms to the accurate unstructured road tracking.