| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 99 === A vision-based autonomous vehicle system for use as a machine guide dog in outdoor sidewalk environments is proposed. A vehicle equipped with a two-mirror omni-camera system, which can compute 3D information from acquired omni-images, is used as a test bed. First, an environment learning technique is proposed to construct a navigation map, including a navigation path, along-path landmark locations, and relevant vehicle guidance parameters. Next, a vehicle navigation system with self-localization and automatic guidance capabilities using landmarks on sidewalks including curb lines, hydrants, and light poles is proposed. Based on a space-mapping technique, a new space line detection technique for use on the omni-image directly is proposed, which can compute the 3D position of a vertical space line in the shape of a sidewalk landmark.
Moreover, based on the vertical space line detection technique just mentioned, hydrant and light pole detection and localization techniques are proposed. Also proposed accordingly is a method for vehicle self-localization, which can adjust an imprecise vehicle position caused by incremental mechanic errors to a correct one. In addition, for the purpose to conduct stable and continuous navigation, a curb line following technique is proposed to guide the vehicle along a sidewalk. To avoid obstacles on the navigation path, a new dynamic obstacle detection technique, which uses a ground matching table to localize an obstacle and then avoid it, is proposed. Furthermore, dynamic techniques for exposure and threshold adjustments are proposed for adapting the system’s capability to varying lighting conditions in navigation environments.
Good experimental results showing the flexibility and feasibility of the proposed methods for real applications are also included.
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