Anonymous Feature Processing for Enhanced Navigation

This concept presents a new statistical likelihood function and Bayesian analysis update for non-standard measurement types that rely on associations between observed and cataloged features. These measurement types inherently contain non-standard errors that standard techniques, such as the Kalman filter, make no effort to model, and this mismodeling can lead to filter instability and degraded performance. Vision-based navigation methods utilizing the Kalman filter involve a preprocessing step to identify features within an image by referencing a known catalog. However, errors in this pre-processing can cause navigation failures. AFP offers a new approach, processing points generated by features themselves without requiring identification. Points such as range or bearing are directly processed by AFP. Operating on finite set statistics principles, AFP treats data as sets rather than individual features. This enables simultaneous tracking of multiple targets without feature labeling. Unlike the sequential processing of the Kalman filter, AFP processes updates in parallel, independently scoring each output based on rigorous mathematical functions. This parallel processing ensures robust navigation updates in dynamic environments, and without requiring an identification algorithm upstream of the filter. Computational simulations conducted at Johnson Space Center demonstrate that AFP's performance matches or exceeds that of the ideal Kalman filter, even under non-ideal conditions. Anonymous Feature Processing for Enhanced Navigation is at a technology readiness level (TRL) 4 (component and/or breadboard validation in laboratory environment) and is now available for patent licensing. Please note that NASA does not manufacture products itself for commercial sale. Innovators at NASA Johnson Space Center have developed an innovative algorithmic and computational approach to vision-based feature recognition called Anonymous Feature Processing (AFP). The ‘anonymous’ approach allows feature-based navigation techniques to be performed without the need for explicit correspondence/identification between visual system observations and cataloged map data, thus helping to eliminate costs and risks induced by identification procedures. By eliminating the error-prone and computationally burdensome identification and detection steps, AFP is designed to yield marked improvements in system robustness along with reducing algorithmic and software development costs. This novel method only requires a simple camera, or LIDAR sensor, and flight computer to track multiple targets and navigate vehicles more quickly, reliably, and safely. The AFP approach is adaptable to a wide range of sensor types and platforms, capable of supporting challenging space exploration and terrestrial navigation systems with low visibility conditions or with cluttered surroundings. A new technique such as AFP that eliminates preprocessing while adding system robustness could have commercial applications in autonomous vehicles, manufacturing, and research-based imaging.