Benchmarking Visual Odometry Algorithms on TUM and ETH3D Datasets

We conducted extensive experiments on the TUM and ETH3D datasets to evaluate the performance of various visual odometry algorithms. These datasets are widely recognized in the field and provide a standardized framework for comparing different algorithms. Our experiments covered a range of challenging scenarios, including dynamic environments, low-texture scenes, and varying lighting conditions. The results provide valuable insights into the strengths and weaknesses of different algorithms and highlight the importance of selecting an appropriate algorithm based on the specific application requirements.