Improved Autonomous Navigation Through Optimal Sensor Outliers

One of the greatest challenges associated with pose estimation using cameras or sensors such as LIDAR is removal of sensor outliers. Outliers exist for numerous reasons, such as incorrectly associated feature points, extraneous data points, etc. Without proper removal of outliners, an incorrect pose estimate may result, which can lead to difficulty in achieving mission goals for autonomous operations. A common approach to remove outlines is the Random Sample Consensus (RANSAC) algorithm, which is an iterative and non-deterministic algorithm. RANSAC may not be optimal, especially if an incorrect model is used, thus leading to outliers that may pass the RANSAC test but may degrade the pose estimate. The XAnalytix Systems team has developed an optimal closed-form approach to replace the RANSAC algorithm. It is optimal in that it is based on using the statistical properties of the sensor error in its derivation. The heart of the solution is based on an optimally derived pose estimation solution from a Total Least Squares (TLS) approach. A byproduct of the TLS solution is the error-covariance of the sensor residuals. The error-covariance is the key to remove outliers. Because the error-covariance is optimal, it is believed that using it will result in a more robust approach to remove outliers than the standard RANSAC algorithm and its variants. The proposed effort will focus on studying the effectiveness of the newly derived closed-form error-covariance in a new RANSAC-type algorithm, called the Statistical Optimal RANSAC (SO-RANSAC) algorithm. It is expected that at the completion of Phase I the optimal nature of the SO-RANSAC algorithm, compared to traditional RANDAC-type algorithms, will be verified through simulation testing within a realistic test environment. This initial testing and hardware configuration will be used to expedite prototyping and system tests to be conducted during Phase II.

NASA has flown several formation flying missions, such A-Train and Cluster. Also, applications involving proximity operations are of great interest to NASA, as well as safe, precision landing on small bodies. The proposed technology further advances current navigation applications related to all these applications since it provides a robust solution for noncooperative objects. The application is based on rigorously derived error definitions, so that physically correct uncertainty bounds are provided for feature outlier mitigation.

Non-NASA applications, especially DoD ones, are heavily focusing on anti-jamming communication and navigation systems, such as GPS-less navigation. The proposed technology can significantly advance these focus areas because it is self-contained and decreases the susceptibility to outside attacks.

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