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Robust and Adaptive Multi-Camera Visual Odometry
The project aims to develop a robust and adaptive multi-camera visual odometry pipeline based on the existing framework in our lab.
Most visual odometry algorithms are designed to work with monocular cameras and/or stereo cameras. One way to improve the robustness of visual odometry is to use more cameras (3 to N). While it is relatively easy to make visual odometry work with multiple cameras for a specific type of configuration, developing an adaptive solution that works with arbitrary camera configurations (i.e., without changing the code) and that is robust to failures (i.e., if one camera fails during the execution, the algorithm can still proceed) is not straightforward.
Most visual odometry algorithms are designed to work with monocular cameras and/or stereo cameras. One way to improve the robustness of visual odometry is to use more cameras (3 to N). While it is relatively easy to make visual odometry work with multiple cameras for a specific type of configuration, developing an adaptive solution that works with arbitrary camera configurations (i.e., without changing the code) and that is robust to failures (i.e., if one camera fails during the execution, the algorithm can still proceed) is not straightforward.
The project aims to develop a robust and adaptive multi-camera visual odometry pipeline based on the existing framework in our lab.
The project aims to develop a robust and adaptive multi-camera visual odometry pipeline based on the existing framework in our lab.