Informed Data Selection and Integrity Monitoring for Visual SLAM
Visual navigation algorithms pose many difficult challenges which must be overcome in order to achieve mass deployment. State-of-the-art methods are susceptible to error when measurements are noisy or corrupted, and prone to failure when the camera undergoes degenerate motions. The visual navigation problem increases in difficulty when implemented on a micro aerial vehicle (MAV), as the low payload requires the use of computation platforms with limited processing power. In this project, we apply Parity Space Outlier Rejection and Entropy-Based Keyframe Selection to increase the algorithms' accuracy, decrease their run-time, and improve the system's ability to localize.
