Adaptive appearance based loop-closing in heterogeneous environments

Majdik,; Galvez-Lopez,; Lazea,; Castellanos,

doi:10.1109/iros.2011.6048213

Cited by 2 publications

(3 citation statements)

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“…Many of them were developed for groundrobot Simultaneous Localization and Mapping (SLAM) systems to address the loop-closing problem (Cummins and Newman, 2011;Majdik et al, 2011;Galvez-Lopez and Tardos, 2012;Maddern et al, 2012), while other works focused on position tracking using the Bayesian fashion-such as in (Vaca-Castano et al, 2012), where the authors presented a method that also uses Street View data to track the geospatial position of a camera-equipped car in a city-like environment. Other algorithms used image-search-based localization for hand-held mobile devices to detect Point Of Interest (POI), such as landmark buildings or museums (Baatz et al, 2012;Fritz et al, 2005;Yeh et al, 2004).…”

Section: Related Workmentioning

confidence: 99%

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Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles

et al. 2015

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In this paper, we address the problem of globally localizing and tracking the pose of a cameraequipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image-based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air-ground image-matching algorithm to search the airborne image of the MAV within a ground-level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back-projecting the corresponding image points onto a cadastral threedimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground-level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision-based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus outperforming conventional visual place-recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera. Abstract-In this paper, we address the problem of globally localizing and tracking the pose of a camera-equipped Micro Aerial Vehicle (MAV) flying in urban streets at low altitudes without GPS. An image-based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air-ground image matching algorithm to search the airborne image of the MAV within a ground-level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back-projecting the corresponding image points onto a cadastral 3D city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground-level images. The proposed approach is tested on a 2km trajectory with a small quadroctopter flying in the streets of Zurich. Our vision-based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera.

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Section: Related Workmentioning

confidence: 99%

“…The similarity between two images, described by the BoW vectors is estimated by counting the common visual words in the images. Different weighting strategies can be adopted between the words of the visual vocabulary (Majdik et al, 2011). The results of this approach applied to the air-ground dataset are shown in Fig.…”

Section: ) Bag-of-words Search Algorithmsmentioning

confidence: 99%

Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles

et al. 2015

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“…The similarity between two images, described by the BoW vectors is estimated by counting the common visual words in the images. Different weighting strategies can be adopted between the words of the visual vocabulary [6]. The results of this approach applied to the air-ground dataset are shown in Fig.…”

Section: B Bag-of-words Search Algorithmsmentioning

confidence: 99%

MAV urban localization from Google street view data

Majdik

Albers-Schoenberg

Scaramuzza

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches. MULTIMEDIA MATERIALPlease note that this paper is accompanied by a video demonstration available on our webpage along with the dataset used in this work: rpg.ifi.uzh.ch

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Adaptive appearance based loop-closing in heterogeneous environments

Cited by 2 publications

References 0 publications

Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles

Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles

MAV urban localization from Google street view data

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