Towards orbital based global rover localization

Boukas, Evangelos; Γαστεράτος, Αντώνιος; Visentin, Gianfranco

doi:10.1109/icra.2015.7139591

Cited by 23 publications

(14 citation statements)

References 29 publications

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“…The proposed system (Fig. ) comprises both an offline and an onboard procedure . The offline procedure is meant for an ex‐ante run and its output is adequately compact (labeled points of a network) suitable to fit into the onboard memory of a rover.…”

Section: System Architecturementioning

confidence: 99%

“…2) comprises both an offline and an onboard procedure. 58 The offline procedure is meant for an ex-ante run and its output is adequately compact (labeled points of a network) suitable to fit into the onboard memory of a rover. Firstly, a high-quality orthorectified georeferenced image-for example, a High Resolution Imaging Science Experiment (HiRISE) 59 vector and a k-NN classifier.…”

Section: System Architecturementioning

confidence: 99%

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Introducing a globally consistent orbital‐based localization system

Boukas

Γαστεράτος

Visentin³

2017

Journal of Field Robotics

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In spite of the good performance of space exploratory missions, open issues still await to be solved. In autonomous or composite semi‐autonomous exploration of planetary land surfaces, rover localization is such an issue. The rovers of these missions (e.g., the MER and MSL) navigate relatively to their landing spot, ignoring their exact position on the coordinate system defined for the celestial body they explore. However, future advanced missions, like the Mars Sample Return, will require the localization of rovers on a global frame rather than the arbitrarily defined landing frame. In this paper we attempt to retrieve the absolute rover's location by identifying matching Regions of Interest (ROIs) between orbital and land images. In particular, we propose a system comprising two parts, an offline and an onboard one, which functions as follows: in advance of the mission a Global ROI Network (GN) is built offline by investigating the satellite images near the predicted touchdown ellipse, while during the mission a Local ROI Network (LN) is constructed counting on the images acquired by the vision system of the rover along its traverse. The last procedure relies on the accurate VO‐based relative rover localization. The LN is then paired with the GN through a modified 2D DARCES algorithm. The system has been assessed on real data collected by the ESA at the Atacama desert. The results demonstrate the system's potential to perform absolute localization, on condition that the area includes discriminative ROIs. The main contribution of this work is the enablement of global localization performed on contemporary rovers without requiring any additional hardware, such as long range LIDARs.

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Section: System Architecturementioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

Introducing a globally consistent orbital‐based localization system

Boukas

Γαστεράτος

Visentin³

2017

Journal of Field Robotics

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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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“…Ground‐based rover prototypes aimed at supporting future planetary exploration missions often have far greater design freedom in terms of hardware choices, such as the inclusion of Lidars, omnidirectional cameras, and faster processors onboard. Several studies on rover global localization used a matching of rover locally mapped terrain (using Lidar or stereovision data) and the orbiter generated Digital Elevation Model . The use of real‐time Simultaneous Localization and Mapping (SLAM) methods has also been proposed for future rovers .…”

Section: Introductionmentioning

confidence: 99%

“…Several studies on rover global localization used a matching of rover locally mapped terrain (using Lidar or stereovision data) and the orbiter generated Digital Elevation Model. [33][34][35][36][37] The use of real-time Simultaneous Localization and Mapping (SLAM) methods has also been proposed for future rovers. [38][39][40] In particular, Das et al 41 from the University of Waterloo competed in the NASA SRR challenge using a robot equipped with a 3D Lidar based real-time pose graph SLAM.…”

Section: Introductionmentioning

confidence: 99%

Cataglyphis: An autonomous sample return rover

Ohi

Lassak

et al. 2017

Journal of Field Robotics

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This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth‐specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under‐actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.

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Towards orbital based global rover localization

Cited by 23 publications

References 29 publications

Introducing a globally consistent orbital‐based localization system

Introducing a globally consistent orbital‐based localization system

Collaborative localization of aerial and ground robots through elevation maps

Cataglyphis: An autonomous sample return rover

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