Engineering topics
Vaughan, Geoffrey
Publications and source records attributed to Vaughan, Geoffrey.
Mars 2020 Lander Vision System Flight Performance 1
The Mars 2020 Entry Descent and Landing (EDL) system delivered the Perseverance rover to the surface of Mars on February 18th, 2021. A large fraction of the Jezero Crater landing site was covered with landing hazards including cliffs, inescapable dune fields and rocks. These hazards were identified or inferred using orbital imagery before launch so that they could be avoided using Terrain Relative Navigation (TRN) which was composed of two parts: the Lander Vision System (LVS) and Safe Target Selection (STS). During EDL, the LVS successfully estimated map relative position by fusing landmarks matched between descent imagery and a map of the landing site with Inertial Measurement Unit (IMU) data. This position estimate was used by STS to identify the safest target for landing that was also reachable given fuel and other constraints. The EDL system then used the powered descent phase to retarget to this location and land safely. The overall error between the targeted location and actual landing location was 5m which was an order of magnitude less than the 60m touchdown error requirement. This paper will describe the final tests of the LVS before launch, the checkout of the LVS during operations and the LVS performance during EDL.
Design and Analysis of Map Relative Localization for Access to Hazardous Landing Sites on Mars
Human and robotic planetary lander missions require accurate surface relative position knowledge to land near science targets or next to pre-deployed assets. In the absence of GPS, accurate position estimates can be obtained by automatically matching sensor data collected during descent to an on-board map. The Lander Vision System (LVS) that is being developed for Mars landing applications generates landmark matches in descent imagery and combines these with inertial data to estimate vehicle position, velocity and attitude. This paper describes recent LVS design work focused on making the map relative localization algorithms robust to challenging environmental conditions like bland terrain, appearance differences between the map and image and initial input state errors. Improved results are shown using data from a recent LVS field test campaign. This paper also fills a gap in analysis to date by assessing the performance of the LVS with data sets containing significant vertical motion including a complete data set from the Mars Science Laboratory mission, a Mars landing simulation, and field test data taken over multiple altitudes above the same scene. Accurate and robust performance is achieved for all data sets indicating that vertical motion does not play a significant role in position estimation performance.