Mars rovers localization by matching local horizon to surface digital elevation models

Chiodini, Sebastiano; Pertile, Marco; Debei, S.; Bramante, Lorenzo; Ferrentino, Enrico; Villa, Alfredo Giovanni; Musso, Ivano; Barrera, Marco

doi:10.1109/metroaerospace.2017.7999600

Cited by 28 publications

(15 citation statements)

References 10 publications

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“…There are a few panoramic skyline localization methods that are quite different due to their application in different scenarios. For example, in [10] a panoramic skyline localization method is used on Mars exploration vehicles, as illustrated in Figure 1. After capturing the panoramic image, the extracted skyline is calibrated by using the high-precision orientation sensor.…”

Section: Panoramic Skyline Localizationmentioning

confidence: 99%

“…Due to the large amount of data, existing methods only locate the panoramic skylines in a small area (less than 1 km 2 ) in order to improve the retrieval efficiency, and use sensor (e.g., as in [10]) or other special features as in [9] to obtain the camera orientation. In this paper, a clustering method based on lapel points is first proposed, and a lapel point matching algorithm is designed to improve the efficiency of skyline retrieval.…”

Section: Panoramic Skyline Matchingmentioning

confidence: 99%

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Fast Geo-Location Method Based on Panoramic Skyline in Hilly Area

Pan

Tang

Tjahjadi

et al. 2021

IJGI

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Localization method based on skyline for visual geo-location is an important auxiliary localization method that does not use a satellite positioning system. Due to the computational complexity, existing panoramic skyline localization methods determine a small area using prior knowledge or auxiliary sensors. After correcting the camera orientation using inertial navigation sensors, a fine position is achieved via the skyline. In this paper, a new panoramic skyline localization method is proposed that involves the following. By clustering the sampling points in the location area and improving the existing retrieval method, the computing efficiency of the panoramic skyline localization is increased by fourfold. Furthermore, the camera orientation is estimated accurately from the terrain features in the image. Experimental results show that the proposed method achieves higher localization accuracy and requires less computation for a large area without the aid of external sensors.

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Section: Panoramic Skyline Localizationmentioning

confidence: 99%

Section: Panoramic Skyline Matchingmentioning

confidence: 99%

Fast Geo-Location Method Based on Panoramic Skyline in Hilly Area

Pan

Tang

Tjahjadi

et al. 2021

IJGI

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show abstract

“…A method for accurate registration of low variance horizon profiles is proposed by (Grelsson et al, 2016), but they only estimate the camera orientation and not the position. Another method suitable for low variance horizon lines is proposed by (Chiodini et al, 2017), where a Mars rover is localized by matching the detected skyline with DEM data. For position estimation, they do a grid search over the location and the viewing angle, and minimize the least-square error between the detected and the rendered skyline.…”

Section: Related Workmentioning

confidence: 99%

GPS‐level accurate camera localization with HorizonNet

Grelsson

Robinson

Felsberg

et al. 2019

Journal of Field Robotics

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This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 • panoramic image around the USV.We design a CNN architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model (DEM) data in the Fourier domain using a MOSSE correlation filter.Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in field trials conducted over three days in the archipelago. Our approach provides excellent results by achieving robust position estimates with GPS-level accuracy in previously unvisited test areas.

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“…Skyline‐based solutions (Cozman & Krotkov, 1997; Stein & Medioni, 1992) were presented in the early 1990s as a means of finding a coarse (in the order of hundred meters) localization of the rover from an unknown initial state. VIPER (Chiodini et al, 2017; Cozman, Krotkov, & Guestrin, 2000) is a well‐known algorithm that matches the horizon skyline signature captured by a panoramic picture acquired by the rover, to predicted skyline signatures at various positions on the global map. Feature‐based solutions later presented were capable of showing higher precision in global localization (up to a few meters).…”

Section: Introductionmentioning

confidence: 99%

SLAM for autonomous planetary rovers with global localization

Geromichalos

Azkarate

Tsardoulias

et al. 2020

Journal of Field Robotics

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This paper describes a novel approach to simultaneous localization and mapping (SLAM) techniques applied to the autonomous planetary rover exploration scenario to reduce both the relative and absolute localization errors, using two well‐proven techniques: particle filters and scan matching. Continuous relative localization is improved by matching high‐resolution sensor scans to the online created local map. Additionally, to avoid issues with drifting localization, absolute localization is globally corrected at discrete times, according to predefined event criteria, by matching the current local map to the orbiter's global map. The resolutions of local and global maps can be appropriately chosen for computation and accuracy purposes. Further, the online generated local map, of the form of a structured elevation grid map, can also be used to evaluate the traversability of the surrounding environment and allow for continuous navigation. The objective of this study is to support long‐range low‐supervision planetary exploration. The implemented SLAM technique has been validated with a data set acquired during a field test campaign performed at the Teide Volcano on the island of Tenerife, representative of a Mars/Moon exploration scenario.

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Mars rovers localization by matching local horizon to surface digital elevation models

Cited by 28 publications

References 10 publications

Fast Geo-Location Method Based on Panoramic Skyline in Hilly Area

Fast Geo-Location Method Based on Panoramic Skyline in Hilly Area

GPS‐level accurate camera localization with HorizonNet

SLAM for autonomous planetary rovers with global localization

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