Mars 2020 Lander Vision System Flight Performance

Johnson, Andrew; Aaron, Seth; Ansari, Homayoon; Bergh, Charles F.; Bourdu, Helene; Butler, Jim D.; Chang, Jonathan; Cheng, R. K.; Cheng, Yang; Clark, Kenneth P.; Clouse, Daniel S.; Donnelly, Rob; Gostelow, Kim P.; Jay, W Ponder; Jordan, Michael C.; Mohan, Swati; Montgomery, Jacqueline; Morrison, Jack; Schroeder, Steven R.; Shenker, Boris; Sun, George; Trawny, Nikolas; Umsted, Carson; Vaughan, Geoffrey M.; Ravine, M. A.; Schaffner, J. A.; Shamah, J. M.; Zheng, Jason

doi:10.2514/6.2022-1214

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…Recently, Downes et al [9] present a deep learning method for lunar crater detection to improve TRN landmark tracking. The Lander Vision System (LVS) [10] used for the Mars 2020 mission uses vision-based landmark matching starting at an altitude of 4200m above the martian surface with the objective of achieving less than 40m error with respect to the landing site. Our analysis focuses on higher altitudes and on a larger span on altitudes (4.5 km to 33 km for the balloon dataset).…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Terrain Relative Navigation on High Altitude Balloon and Sub-Orbital Rocket

Maggio

Mario

Streetman

et al. 2023

AIAA SCITECH 2023 Forum

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We present an experimental analysis on the use of a camera-based approach for highaltitude navigation by associating mapped landmarks from a satellite image database to camera images, and by leveraging inertial sensors between camera frames. We evaluate performance of both a sideways-tilted and downward-facing camera on data collected from a World View Enterprises high-altitude balloon with data beginning at an altitude of 33 km and descending to near ground level (4.5 km) with 1.5 hours of flight time. We demonstrate less than 290 meters of average position error over a trajectory of more than 150 kilometers. In addition to showing performance across a range of altitudes, we also demonstrate the robustness of the Terrain Relative Navigation (TRN) method to rapid rotations of the balloon, in some cases exceeding 20 • per second, and to camera obstructions caused by both cloud coverage and cords swaying underneath the balloon. Additionally, we evaluate performance on data collected by two cameras inside the capsule of Blue Origin's New Shepard rocket on payload flight NS-23, traveling at speeds up to 880 km/h, and demonstrate less than 55 meters of average position error.

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Section: Related Workmentioning

confidence: 99%

Vision-Based Terrain Relative Navigation on High Altitude Balloon and Sub-Orbital Rocket

Maggio

Mario

Streetman

et al. 2023

AIAA SCITECH 2023 Forum

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“…However, given weight and size constraints, range sensors are not suitable for a weight restricted UAV. The Mars 2020 mission deploys LVS, a lander vision system to detect landing hazards [14]. Given an on-board map with predetermined hazard locations, LVS uses a monocular camera to estimate the spacecraft's position during descent and triggers an avoidance maneuver if necessary.…”

Section: Related Workmentioning

confidence: 99%

Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft

Schoppmann¹,

Proença²,

Delaune³

et al. 2021

Preprint

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In this paper, we propose a resource-efficient approach to provide an autonomous UAV with an on-board perception method to detect safe, hazard-free landing sites during flights over complex 3D terrain. We aggregate 3D measurements acquired from a sequence of monocular images by a Structure-from-Motion approach into a local, robot-centric, multi-resolution elevation map of the overflown terrain, which fuses depth measurements according to their lateral surface resolution (pixel-footprint) in a probabilistic framework based on the concept of dynamic Level of Detail. Map aggregation only requires depth maps and the associated poses, which are obtained from an on-board Visual Odometry algorithm. An efficient landing site detection method then exploits the features of the underlying multi-resolution map to detect safe landing sites based on slope, roughness, and quality of the reconstructed terrain surface. The evaluation of the performance of the mapping and landing site detection modules are analyzed independently and jointly in simulated and real-world experiments in order to establish the efficacy of the proposed approach.

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“…Recent decades have witnessed great technological advancement and expanding acceptance of autonomous vision-based navigation for the exploration of other celestial bodies (e.g., Moon, Mars, asteroids). These advancements in real-time, on-board OPNAV are exemplified by the technological progression from simple estimation of lander velocity with the Mars Exploration Rover's DIMES system in 2004 [18] to autonomous feature tracking that will soon be demonstrated on the OSIRIS-REx mission to asteroid Bennu [101] and during landing of the Mars Perseverance (Mars 2020) rover [61].…”

Section: Introductionmentioning

confidence: 99%

“…Many landmark-based OPNAV algorithms rely on the real-time rendering of an onboard digital elevation map (DEM) [101,3,41,61]. The standard approach is to render the expected appearance of landmark patches and then compare this to regions of the navigation image, usually by means of a 2D crosscorrelation.…”

Section: Introductionmentioning

confidence: 99%

“…Of note, we know that no such invariant exists for an arbitrary patch of terrain with constant albedo and with an approximately Lambertian reflectance [16]. This suggests that terrain patches (or maplets)-as is often used in correlation-based TRN [101,3,41,61]-may not be well-suited for lostin-space TRN, though this is an important topic of future work. We also observe that popular image feature descriptors-such as SIFT [79], SURF [7], BRIEF [74], ORB [119], and others-do not describe the type of invariance required for lost-in-space TRN.…”

Section: Introductionmentioning

confidence: 99%

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Lunar Crater Identification in Digital Images

Christian,

Derksen,

Watkins

2020

Preprint

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It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

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Mars 2020 Lander Vision System Flight Performance

Cited by 20 publications

References 8 publications

Vision-Based Terrain Relative Navigation on High Altitude Balloon and Sub-Orbital Rocket

Vision-Based Terrain Relative Navigation on High Altitude Balloon and Sub-Orbital Rocket

Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft

Lunar Crater Identification in Digital Images

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