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Smart Feature Selection In Visual Odometry
The student is expected to study how motion estimation is affected by feature selection (e.g., number of features, different feature locations). The ultimate goal will be to implement a smart feature selection mechanism in our visual odometry framework.
For most robotic platforms, computational resources are usually limited. Therefore, ideally, algorithms running onboard should be adaptive to the available computational power. For visual odometry, the number of features largely decides the resource the algorithm needs.
By using a selected subset of features, we can reduce the required computational resource without losing accuracy significantly.
For most robotic platforms, computational resources are usually limited. Therefore, ideally, algorithms running onboard should be adaptive to the available computational power. For visual odometry, the number of features largely decides the resource the algorithm needs.
By using a selected subset of features, we can reduce the required computational resource without losing accuracy significantly.
The project aims to study the problem of smart feature selection for visual odometry.
The student is expected to study how motion estimation is affected by feature selection (e.g., number of features, different feature locations). The ultimate goal will be to implement a smart feature selection mechanism in our visual odometry framework.
The project aims to study the problem of smart feature selection for visual odometry.
The student is expected to study how motion estimation is affected by feature selection (e.g., number of features, different feature locations). The ultimate goal will be to implement a smart feature selection mechanism in our visual odometry framework.