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Informative feature selection for multi-camera SLAM
Multi camera SLAM system are more robust and accurate, but demand more computation resources. The goal of this project is to design feature sampling methods from multiple cameras that ensures efficient use of computational resources while ensuring robustness of SLAM.
Keywords: SLAM, VIO, Multi Camera, computer vision, robotics
Adding more cameras to the system increases the robustness and
accuracy of SLAM systems, but demands more computation resources. The
computation cost increases in general with the number of cameras and the number of
features per camera. However, all the camera features do not necessarily contribute
towards pose estimation. We therefore look into feature selection that samples from all
features only those that are relevant for SLAM.Thus we are able to gain performance, in
terms of time, without compromising in accuracy. Selecting features for a general
multiple camera setup is not an easy problem: e.g., we need to consider the distance,
distribution of the landmarks, time that the landmarks can be tracked/visible (this is
coupled with the motion of the robot).
In this project, we would explore different informative feature selection and evaluate their
performance with a multi-camera system consisting of maximum 10 cameras.
Adding more cameras to the system increases the robustness and accuracy of SLAM systems, but demands more computation resources. The computation cost increases in general with the number of cameras and the number of features per camera. However, all the camera features do not necessarily contribute towards pose estimation. We therefore look into feature selection that samples from all features only those that are relevant for SLAM.Thus we are able to gain performance, in terms of time, without compromising in accuracy. Selecting features for a general multiple camera setup is not an easy problem: e.g., we need to consider the distance, distribution of the landmarks, time that the landmarks can be tracked/visible (this is coupled with the motion of the robot). In this project, we would explore different informative feature selection and evaluate their performance with a multi-camera system consisting of maximum 10 cameras.
The goal is to design information theoretic-based feature sampling methods that
ensure efficient use of computational resources while preserving the accuracy of the
SLAM system
The goal is to design information theoretic-based feature sampling methods that ensure efficient use of computational resources while preserving the accuracy of the SLAM system