| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 106 === With the development of autonomous vehicle, vehicle localization becomes more and more significant. Localization can be achieved by many different sensors, including LIDAR, GPS, IMU and camera. However, GPS is inaccurate when GPS signal is blocked by external environment. LIDAR is too expensive and cumulative error exist from IMU. Camera is appropriate for localization. To estimate relative pose of camera, visual odometry is used. But there is cumulative error in visual odometry after using for a while. To obtain a more accurate localization result, a global map with recorded road marks can be used.
In visual odometry part, monocular camera is used in research. However, translation movement scale cannot be obtained by monocular visual odometry. Translation is obtained from GPS or IMU in research. Kinematic model for ground vehicle is used to obtain planner motion. To reduce cumulative error, every frame is used to estimate motion relative to keyframe.
In localization based on road mark part, road mark detection based on machine learning is presented. But it takes a lot of time to do road mark detection. Road mark tracking based on prior information is also present. Computational time in road mark tracking is one eighth of computational time in road mark detection. To avoid unnecessary road mark detection, road mark detection is active or not based on car position on map. If car is nearby road mark, road mark detection is active.
To combine car position information from two source, Kalman filter is used. But motion model is nonlinear. Unscented Transform is used to solve nonlinear transformation of Gaussian distribution. The experiment shows that result from visual odometry sometimes is not precise due to inaccurate translation movement. But result can be fixed by measurement from detected road mark in Unscented Kalman filter. Road mark detection can find road mark in consistent light condition.
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