Navigational Road Modeling Based On Omnidirectional Multi-camera System

The vision-based navigation is an important way of the navigation of the Autonomous Land Vehicles (ALVs). However, due to the limited Field of View (FOV), the ordinary vision sensors are unable to perceive the global information of the roads, especially the road intersections. This causes the limitations to the vision-based navigation.The Omnidirectional Multi-camera System (OMS) has advantages of large FOV, high resolution and slight distortion. Taking the OMS as the vision sensor for the navigation, this dissertation places emphasis on the study of the detection, the structural estimation and the modeling of an intersection and the planning of the referential path and the limited speed in the ALV’s navigation. The study is based on the mathematical tools of the Radon Transform, the Gaussian Mixture Model (GMM), the Markov Random Field (MRF) and the Graph Cuts.The main contents and contributions of this dissertation are as follows:1. We propose the algorithms for solving the calibration and the rectification for an OMS. The extrinsic parameters are important to determine the pose of the navigation system. Firstly, the initial guesses of the extrinsic parameters are calculated. Then the final results are refined with the Levenberg-Marquardt algorithm. We have analysed the validity and reliability of the algorithms.2. For detecting the roads, we introduce a kind of road detection algorithm based on the GMM and the MRF. Although the ordinary road detection algorithms based on the probability and the machine learning take the relation and the influence between the frames into account, they ignore the spatial relationship between the pixels, thus resulting in the over-segmentation. This algorithm treats the road segmentation as a binary classification problem. Through the GMM and the machine learning we can obtain the probability density field of the road and the non-road area of the image. By the MRF and the Graph Cuts, the optimized labels of the road and the non-road can be calculated. 3. This dissertation also puts forward the parametric model and the data structure of the road intersection and studies the regression algorithm of the model at the same time. In the ALV’s navigation in the intersection region, people often need to descript and store the shape and the structure information of the intersection. Suffering from the big data, the non-parametric methods are not suitable for the processing and the storage. To solve these problems, we propose the parametric model of the intersection. The model has a strong adaptability and flexibility, using the inner envelope and the boundary lines of the roads to describe the intersection. On the one hand, the boundary line model records the actual information of the road boundary itself. On the other hand, the inner envelope model guarantees the safety of the vehicle, mainly used for the navigation and the path planning.4. This study also presents a road intersection detection and recognition algorithm based on the Radon Transform, and implements the planning of the referential path and the limited speed based on the parametric model of the road intersection. The intersection detection is an important problem of the ALV’s navigation in the intersection region. It decides the basic judgement to the road structure. Among the existing methods of the intersection detection, some of them only detect a certain type of the intersections and some are based on a strict condition or hypothesis, thus causing some limitations. The detection and recognition algorithm in this study is able to detect multiple types of the intersection, and recognize the shape and the structure. After modeling the intersection, the algorithm also plans the referential path, the radius and the limited speed of the ALV’s turning.