Design through operation of an image-based velocity estimation system for mars landing

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Abstract

During the Mars Exploration Rover (MER) landings, the Descent Image Motion Estimation System (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combined measurements from a descent camera, a radar altimeter, and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm used altitude and attitude measurements to rectify images to a level ground plane. Feature selection and tracking were employed in the rectified images to compute the horizontal motion between images. Differences of consecutive motion estimates were then compared to inertial measurements to verify correct feature tracking. DIMES combined sensor data from multiple sources in a novel way to create a low-cost, robust, and computationally efficient velocity estimation solution, and DIMES was the first robotics vision system used to control a spacecraft during planetary landing. This paper presents the design and implementation of the DIMES algorithm, the assessment of the algorithm performance using a high fidelity Monte Carlo simulation, validation of performance using field test data and the detailed results from the two landings on Mars. DIMES was used successfully during both MER landings. In the case of Spirit, had DIMES not been used onboard, the total velocity would have been at the limits of the airbag capability. Fortunately, DIMES computed the actual steady state horizontal velocity and it was used by the thruster firing logic to reduce the total velocity prior to landing. For Opportunity, DIMES computed the correct velocity, and the velocity was small enough that the lander performed no action to remove it. © 2007 Springer Science+Business Media, LLC.

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APA

Johnson, A., Willson, R., Cheng, Y., Goguen, J., Leger, C., Sanmartin, M., & Matthies, L. (2007). Design through operation of an image-based velocity estimation system for mars landing. International Journal of Computer Vision, 74(3), 319–341. https://doi.org/10.1007/s11263-006-0022-z

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