Terrain-Relative Navigation for Precision Landings based on a Hierarchical Localization Approach

Terrain-relative navigation (TRN) refers to a class of algorithms that can be used to obtain the precise location of a vehicle in a GPS-denied environment by using a pre-computed terrain map. This map consists of recognizable landmarks that can be used to establish correspondences between spacecraft measurements and the terrain. Although several TRN approaches have been successfully implemented in the past, there is still a need for robust, flexible, and efficient TRN algorithms capable of supporting localization across wide trajectory paths without the need for additional sensor measurements. In this work, a new TRN system based on hierarchical localization is designed and evaluated. The proposed system aims to take advantage of recent advances in algorithms for autonomous navigation in terrestrial environments to improve the efficiency over previous TRN implementations. An extensive evaluation of the proposed system is conducted using a lunar imagery dataset. To assess the feasibility of deploying the proposed TRN system in a real scenario, the system is implemented using a Xilinx ZC706 development board that resembles the computation capabilities of relevant space platforms. Experimental results show that the system is capable of localizing over 90% of images featuring a wide range of scale, rotation, and illumination changes. In addition, leveraging the hardware acceleration capabilities of the FPGA contained within the target platform allows for processing of images at a rate of at least 25 FPS, which is sufficient for real-time operation