Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing

By Anastasios I. Mourikis, Nikolas Trawny, Stergios I. Roumeliotis, Andrew E. Johnson, Adnan I. Ansar, Larry H. Matthies. IEEE Transactions on robotics, 2009

374 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e58ce0cde985c1eada9e4d62d81fc89f408780bf

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In Aerospace Engineering

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Citations in the corpus

Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration (2011, background): “A variety of hybrid visual–inertial systems have been developed, for applications from short-range sensor localization (Strelow and Singh 2002) to long-range motion estimation for spacecraft entry, descent and landing (Mourikis et al. 2009).”
A Theoretical Foundation of Network Localization and Navigation (2018, background): “Localizing mobile nodes with unknown positions (called agents) can be accomplished by utilizing two types of measurements, namely, inter-node and intra-node measurements, together with prior knowledge [54]–[58] Internode measurements refer to those between nodes in a network through radio-frequency ”
Opportunities and challenges with autonomous micro aerial vehicles (2012, background): “Recent results point to algorithms that yield estimates of three-dimensional metric information from just monocular vision combined with a good IMU (Mourikis et al., 2009; Kneip et al., 2011).”
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization (2015, methodology): “To reduce the computational complexity of updating the estimator, all the residuals and Jacobians r = H(x− x̂)+n are stacked to apply measurement compression [33].”
Kalman Filter for Robot Vision: A Survey (2012, methodology): “An EKF can tightly integrate multiple types of visual feature observations with measurements from an IMU [4], [15], [102], [142].”
Visual-Inertial Navigation: A Concise Review (2019, methodology): “Mourikis and Roumeliotis [18] developed one of the earliest successful VINS algorithms, known as the multistate constraint Kalman filter (MSCKF), which later was applied to the application of spacecraft descent and landing [21] and fast UAV autonomous flight [43].”
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems (2012, background): “Another vision-aided inertial TRN system for pinpoint landing on Mars is presented in Mourikis et al. (2009).”
A Direct Least-Squares (DLS) method for PnP (2011, background): “Examples include robot localization [12], spacecraft pose estimation during descent and landing [20], pose determination for model-based vision [17], as well as hand-eye calibration [4].”
Keyframe-based visual–inertial odometry using nonlinear optimization (2015, result): “We therefore compare our results with a competitive implementation of this sliding window filter using on-the-fly feature marginalization as published by Mourikis et al. (2009).”