On February 18, 2021, the Perseverance Rover safely landed on Mars at Jezero Crater. Part of the successful landing was due to the Lander Vision System (LVS), which takes descent images from the LVS Camera (LCAM) and IMU measurements and estimates the lander position relative to a map of the Jezero landing site. The LVS Simulation LCAM (LVSS LCAM) model is an image rendering program developed to test the LVS in a variety of scenarios to ensure performance amid uncertainty. The LVSS LCAM model includes a pointing misalignment model, an exposure timing model, shadowing, a terrain reflectance model, atmospheric attenuation from dust, and sensor effects. This model was used for performance analysis, verification, and validation of the LVS algorithms in a Mars-like simulation prior to landing. This paper describes the LVSS LCAM rendering algorithm and compares flight images from LVS operation during the Perseverance landing with their rendered counterparts.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Camera Simulation for the Perseverance Rover’s Lander Vision System


    Contributors:

    Publication date :

    2022-01-03


    Type of media :

    Preprint


    Type of material :

    No indication


    Language :

    English



    Camera Simulation for Perseverance Rover's Lander Vision System

    Aaron, Seth B. / Cheng, Yang / Trawny, Nikolas et al. | TIBKAT | 2022


    Camera Simulation For Perseverance Rover's Lander Vision System

    Aaron, Seth B. / Cheng, Yang / Trawny, Nikolas et al. | AIAA | 2022


    Evolution of the Mars 2020 Perseverance Rover’s Strategic Planning Process

    Sun, Vivian Z / Sholes, Steven / Stack, Kathryn M et al. | IEEE | 2024


    Development and Execution of the Mars 2020 Perseverance Rover’s Sampling Strategy

    Kronyak, Rachel E. / Kruger, Andrew W. / Sun, Vivian Z. et al. | IEEE | 2024


    Relay Planning in the Perseverance Rover's First 600 Solar Days on Mars

    Young, Emma / Yang, Genevie / Wagner, Travis et al. | IEEE | 2023