Long‐range rover localization by matching LIDAR scans to orbital elevation maps

Carle, Patrick; Furgale, Paul; Barfoot, Timothy D.

doi:10.1002/rob.20336

Cited by 51 publications

(21 citation statements)

References 57 publications

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“…For this purpose, visually detectable landmarks [10] or generated surface elevation maps of the rover surroundings are matched to a global map. The elevation maps are therefore searched for topographic peaks [11] or get compared directly by zeromean normalized cross-correlation [12].…”

Section: A Related Workmentioning

confidence: 99%

Collaborative localization of aerial and ground robots through elevation maps

Käslin¹,

Fankhauser²,

Stumm³

et al. 2016

2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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Collaboration between aerial and ground robots can benefit from exploiting the complementary capabilities of each system, thereby improving situational awareness and environment interaction. For this purpose, we present a localization method that allows the ground robot to determine and track its position within a map acquired by a flying robot. To maintain invariance with respect to differing sensor choices and viewpoints, the method utilizes elevation maps built independently by each robot's onboard sensors. The elevation maps are then used for global localization: specifically, we find the relative position and orientation of the ground robot using the aerial map as a reference. Our work compares four different similarity measures for computing the congruence of elevation maps (akin to dense, image-based template matching) and evaluates their merit. Furthermore, a particle filter is implemented for each similarity measure to track multiple location hypotheses and to use the robot motion to converge to a unique solution. This allows the ground robot to make use of the extended coverage of the map from the flying robot. The presented method is demonstrated through the collaboration of a quadrotor equipped with a downward-facing monocular camera and a walking robot equipped with a rotating laser range scanner. Abstract-Collaboration between aerial and ground robots can benefit from exploiting the complementary capabilities of each system, thereby improving situational awareness and environment interaction. For this purpose, we present a localization method that allows the ground robot to determine and track its position within a map acquired by a flying robot. To maintain invariance with respect to differing sensor choices and viewpoints, the method utilizes elevation maps built independently by each robot's onboard sensors. The elevation maps are then used for global localization: specifically, we find the relative position and orientation of the ground robot using the aerial map as a reference. Our work compares four different similarity measures for computing the congruence of elevation maps (akin to dense, image-based template matching) and evaluates their merit. Furthermore, a particle filter is implemented for each similarity measure to track multiple location hypotheses and to use the robot motion to converge to a unique solution. This allows the ground robot to make use of the extended coverage of the map from the flying robot. The presented method is demonstrated through the collaboration of a quadrotor equipped with a downward-facing monocular camera and a walking robot equipped with a rotating laser range scanner.

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Section: A Related Workmentioning

confidence: 99%

Collaborative localization of aerial and ground robots through elevation maps

Käslin¹,

Fankhauser²,

Stumm³

et al. 2016

2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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“…Using the position of the lidar and the position of the target from GPS, we determined the heading of the lidar to approximately 1 • (one sigma). See Carle et al (2010) for further details. Figure 4 depicts an overview of the lidar scan coverage, and shows the relationship of the lidar scans to the rover traverse from Section 2.…”

Section: Long-range Localization Datamentioning

confidence: 99%

The Devon Island rover navigation dataset

Furgale

Carle

Enright

et al. 2012

The International Journal of Robotics Research

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“…The first is straight visual odometry, with no additional measurements used at any point. The second variation is visual odometry with periodic orientation updates from the sun sensor and inclinometer at the start of each new section (approximately every 500 m), as previously demonstrated by Carle et al (2010). These periodic updates are computed by allowing the vehicle to remain at rest for an extended period of time, in order to collect a large number of measurements.…”

Section: Evaluating the Effects Of The Sun Sensor And Inclinometermentioning

confidence: 99%