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Benchmarking camera control for visual odometry
The project aims to benchmark different camera control algorithms and create related tools.
There are many existing datasets to evaluate the performance of visual odometry algorithms. However, few work has been done in providing a principled way to benchmark the performance of camera control (exposure time/gain) algorithms, which have a large impact on the performance of visual odometry. Most of the current datasets/benchmark tools simply contain images captured at a certain camera configuration, which is not suitable for this purpose. A proper benchmark tool can fill this gap and will be useful for understanding the strengths and weaknesses of different algorithms.
There are many existing datasets to evaluate the performance of visual odometry algorithms. However, few work has been done in providing a principled way to benchmark the performance of camera control (exposure time/gain) algorithms, which have a large impact on the performance of visual odometry. Most of the current datasets/benchmark tools simply contain images captured at a certain camera configuration, which is not suitable for this purpose. A proper benchmark tool can fill this gap and will be useful for understanding the strengths and weaknesses of different algorithms.
The goal of this project is to make use of both synthetic and real data to build a benchmark tool and evaluate the influence of different camera control algorithms on the performance of visual odometry.
The goal of this project is to make use of both synthetic and real data to build a benchmark tool and evaluate the influence of different camera control algorithms on the performance of visual odometry.