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

Mourikis, Anastasios I.; Trawny, Nikolas; Roumeliotis, Stergios I.; Johnson, Andrew; Ansar, Adnan; Matthies, Larry

doi:10.1109/tro.2009.2012342

Cited by 300 publications

(186 citation statements)

References 33 publications

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“…Therefore, LF vision-based navigation algorithm is considered a "visual odometry" system instead of a SLAM [5]. LF navigation is based on a Multi-State Constraint Kalman Filter (MSC-KF) [6]. The MSC-KF is referred to as an opportunistic algorithm, in that it uses any matched image features, but does not attempt to build a database from them for long-term mapping.…”

Section: A Hf Simultaneous Localization and Mappingmentioning

confidence: 99%

Architecture for asymmetric collaborative navigation

Zhu

Roumeliotis

Hesch

et al. 2012

Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium

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Section: A Hf Simultaneous Localization and Mappingmentioning

confidence: 99%

Architecture for asymmetric collaborative navigation

Zhu

Roumeliotis

Hesch

et al. 2012

Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium

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“…It can be done by integrating the INS with an auxiliary navigation subsystem and forming, e.g., such combined system as a visual-aided INS (see, e.g. [22]), which provides the landmark-based estimation of position and velocity. In this case, the extended Kalman filter is proposed to be applied to fuse inertial measurements with camera observations of the socalled map landmarks, i.e., features (e.g., craters) whose coordinates can be found from a map of the landing site, which is available a priori.…”

Section: Navigation Aspects Of the Guidance At Entry Phasementioning

confidence: 99%

Analysis of predictive entry guidance for a Mars lander under high model uncertainties

Kozynchenko¹

2011

Acta Astronautica

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a b s t r a c tThe problem of precision landing on Mars is now considered to be an essential challenge in the planned Mars missions. The paper focused on the guided atmospheric entry as a predominant phase in achieving a desired target state, as compared with the following parachute and powered descent. The predictive algorithms for the longitudinal guidance of a low-lift entry vehicle are treated. The purpose is to investigate applicability of the predictive strategy under possible high discrepancies between the on-board dynamic model and real environment while in entry trajectory. The comparative performance analysis based on computer simulation has been made between the standard one-parametric ''shooting'' predictive algorithm and a more complex two-parametric algorithm providing lower final velocity and, thus, expanding the interval of admissible downrange. However, both algorithms display considerable degradation of downrange accuracy in the cases when the actual drag force is larger than the modelled one. An acceptable solution has been found by including to both predictive guidance schemes an identification algorithm that repeatedly adapts the on-board model to varied environment in real time scale.

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“…The plane-facing cameras can provide a reliable visual information when navigating in environments with poor features or regions with dynamic backgrounds. One interesting application of such system is for precise planetary landing in the spacecraft control systems [1]. Other applications include, e.g., indoor cleaning robots [2], automated parking systems [3], and industrial robots [4] where most of the actions are performed in front of a planar surface.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, the observation of planar features of the ground plane was implicitly used in different visual inertial navigation systems, such as [1], [5]- [8]. Several methods have been proposed in [9], where the properties of the planar features on a horizontal plane are explicitly used to derive the system state-space model; advantages and different aspects in this visual-inertial navigation system including motion estimation, horizontal plane feature detection, and observability analysis have been also addressed in [9].…”

Section: Introductionmentioning

confidence: 99%

Planar-based visual inertial navigation

Panahandeh

Händel

2015

2015 International Conference on Location and GNSS (ICL-GNSS)

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Abstract-This paper investigates the problem of visual-inertial navigation. The proposed navigation system integrates inertial information from an inertial measurement unit (IMU) with visual data from a camera to provide relative pose estimation for a system which is navigating in an unknown structural environment. The main contribution of this paper is derivation of a novel measurement model based on inertial data and visual planar features. The proposed formulation is a solution to the 6-DoF motion estimation where the IMU-camera movement is not restricted over a desired navigation plane. Compared to previous works, which are restricted on using only horizontal plane features, the proposed model is generalized for arbitrary planar features. The theoretical finding of this study is extensively evaluated both with simulation and real world experiments. The presented experiments indicate the reliability of the proposed method to perform accurate 6-DoF pose estimation.

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

Cited by 300 publications

References 33 publications

Architecture for asymmetric collaborative navigation

Architecture for asymmetric collaborative navigation

Analysis of predictive entry guidance for a Mars lander under high model uncertainties

Planar-based visual inertial navigation

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