Visual–inertial navigation for pinpoint planetary landing using scale-based landmark matching

Delaune, Jeff; Besnerais, Guy Le; Voirin, Thomas; Farges, Jean‐Loup; Bourdarias, Clement

doi:10.1016/j.robot.2016.01.007

Cited by 25 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This system was successfully tested on a parachute dropped from a sounding rocket, achieving a landing position estimation error of less than 7 meters [19]. The European Space Agency (ESA) and its partners have developed a similar system, which was demonstrated using a robotic arm and a large simulated lunar surface [20].…”

Section: Terrain Relative Navigationmentioning

confidence: 99%

“…Several landmark-based TRN systems for planetary landing have been proposed in the literature, including approaches using image patches [14], craters [21], SURF features [28], and Harris corners [11], [20] as landmarks. The majority of these do not mention the means they used to select which features to store in their onboard databases, presumably storing all possible landmarks in the region for the best possible nav- igation accuracy.…”

Section: Landmark Selectionmentioning

confidence: 99%

“…Delaune, et. al [20] provide a method for landmark selection only when candidate landmarks are too close to one another, reducing the risk of outlier measurements due to false data associations.…”

Section: Landmark Selectionmentioning

confidence: 99%

“…We assume accurate feature detection and data association for all tests, as these issues have already been well addressed in the literature, and set the landmark recognition probability of (2) to be uniform across all landmarks. Existing feature tracking methods, such as those presented in [14], [20], [21], could integrate with our presented algorithms without modification. An example simulated camera image with landmark observations is shown in Figure 7.…”

Section: Simulation Setupmentioning

confidence: 99%

See 3 more Smart Citations

Graph-based terrain relative navigation with optimal landmark database selection

Steiner

Brady

Hoffman

2015

2015 IEEE Aerospace Conference

View full text Add to dashboard Cite

Terrain relative navigation (TRN) offers a means to constrain absolute vehicle position and attitude without using a GPS receiver or star camera, such as for unmanned aerial vehicles or planetary landing spacecraft, using a pre-computed terrain database of distinctive landmarks in the operational environment. However, depending on the length of the planned trajectory, these terrain landmark databases may grow prohibitively large for the onboard data storage, processing, and communication capabilities of these types of vehicles.In this paper, we introduce a definition of landmark utilitywhich encapsulates the observation probability of a landmark and its spatial distribution relative to nearby landmarks in the database -as a measure of the value of potential line-ofsight measurements to that landmark. We additionally present an efficient algorithm to sort all potential landmarks in an environment based on their relative utility without access to the actual sensor measurements or trajectory. This enables pre-determination of a limited-size terrain landmark database containing the N -best landmarks in the environment, which is applicable to any landmark-based TRN system.To evaluate our landmark selection algorithm, we additionally introduce an incremental smoothing-based approach to TRN using a Bayesian factor graph representation. This system is capable of overcoming several challenges associated with TRN systems, including relinearization of past measurements, simultaneously utilizing pre-mapped and opportunistic features in a common estimator, and efficient sensor fusion in a common, probabilistic framework. We provide results showing the benefits of our utility-based landmark selection compared to alternative selection approaches, using evaluation metrics independent of any particular TRN estimation framework and the navigation performance of our graph-based TRN system.

show abstract

Section: Terrain Relative Navigationmentioning

confidence: 99%

Section: Landmark Selectionmentioning

confidence: 99%

Section: Landmark Selectionmentioning

confidence: 99%

Section: Simulation Setupmentioning

confidence: 99%

See 2 more Smart Citations

Graph-based terrain relative navigation with optimal landmark database selection

Steiner

Brady

Hoffman

2015

2015 IEEE Aerospace Conference

View full text Add to dashboard Cite

show abstract

“…The simulation of the final phase of the landing trajectory is of crucial importance, therefore software tools [4,5] and hardware facilities [6,7] have been used to test the algorithms. Optical Navigation seems to be one of the most promising techniques, see [8][9][10]; in this complex field, the concept of Optical Flow deserves a primary role [11,12].…”

Section: Introductionmentioning

confidence: 99%

Experimental results of a Terrain Relative Navigation algorithm using a simulated lunar scenario

2015

View full text Add to dashboard Cite

This paper deals with the problem of the navigation of a lunar lander based on the Terrain Relative Navigation approach. An algorithm is developed and tested on a scaled simulated lunar scenario, over which a tri-axial moving frame has been built to reproduce the landing trajectories. At the tip of the tri-axial moving frame, a long-range and a short-range infrared distance sensor are mounted to measure the altitude. The calibration of the distance sensors is of crucial importance to obtain good measurements. For this purpose, the sensors are calibrated by optimizing a nonlinear transfer function and a bias function using a least squares method. As a consequence, the covariance of the sensors is approximated with a second order function of the distance. The two sensors have two different operation ranges that overlap in a small region. A switch strategy is developed in order to obtain the best performances in the overlapping range. As a result, a single error model function of the distance is found after the evaluation of the switch strategy. Because of different environmental factors, such as temperature, a bias drift is evaluated for both the sensors and properly taken into account in the algorithm. In order to reflect information of the surface in the navigation algorithm, a Digital Elevation Model of the simulated lunar surface has been considered. The navigation algorithm is designed as an Extended Kalman Filter which uses the altitude measurements, the Digital Elevation Model and the accelerations measurements coming from the moving frame. The objective of the navigation algorithm is to estimate the position of the simulated space vehicle during the landing from an altitude of 3 km to a landing site in the proximity of a crater rim. Because the algorithm needs to be updated during the landing, a crater peak detector is conceived in order to reset the navigation filter with a new state vector and new state covariance. Experimental results of the navigation algorithm are presented in the paper

show abstract

Path Planning Using Particle Swarm Optimization and Fuzzy Logic

Oultiligh¹,

Ayad²,

Elkari³

et al. 2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Visual–inertial navigation for pinpoint planetary landing using scale-based landmark matching

Cited by 25 publications

References 31 publications

Graph-based terrain relative navigation with optimal landmark database selection

Graph-based terrain relative navigation with optimal landmark database selection

Experimental results of a Terrain Relative Navigation algorithm using a simulated lunar scenario

Path Planning Using Particle Swarm Optimization and Fuzzy Logic

Contact Info

Product

Resources

About