Satellite image based precise robot localization on sidewalks

Senlet, Turgay; Elgammal, Ahmed

doi:10.1109/icra.2012.6225352

Cited by 37 publications

(17 citation statements)

References 18 publications

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“…A recent investigation [12] developed the footprint orientation (FPO) descriptor, which is computed from an omnidirectional image, to match 2D urban terrain model that is generated from aerial imagery for estimating the position and orientation of a camera. Senlet [18] combined stereo camera images, visual odometry, satellite map matching, and a sidewalk probability transfer function obtained from street maps to attain globally corrected localization results. Kummerle et al [19] presented a novel SLAM approach that achieved global consistency by utilizing publicly accessible aerial images as priori information.…”

Section: Related Workmentioning

confidence: 99%

Error aware multiple vertical planes based visual localization for mobile robots in urban environments

Wang

Liu

2015

Sci. China Inf. Sci.

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A novel error-aware visual localization method is proposed that utilizes vertical planes, such as vertical building facades in urban areas as landmarks. Vertical planes, reconstructed from coplanar vertical lines, are robust high-level features if compared with point features or line features. Firstly, the error models of vertical lines and vertical planes are built, where maximum likelihood estimation (MLE) is employed to estimate all vertical planes from coplanar vertical lines. Then, the closed-form representation of camera location error variance is derived. Finally, the minimum variance camera pose estimation is formulated into a convex optimization problem, and the weight for each vertical plane is obtained by solving this well-studied problem. Experiments are carried out and the results show that the proposed localization method has an accuracy of about 2 meters, at par with commercial GPS operating in open environments. CitationLi H F, Wang H P, Liu J T. Error aware multiple vertical planes based visual localization for mobile robots in urban environments.

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Section: Related Workmentioning

confidence: 99%

Error aware multiple vertical planes based visual localization for mobile robots in urban environments

Wang

Liu

2015

Sci. China Inf. Sci.

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“…Senlet and Elgammal use satellite images to segment roads [24] and sidewalks [25] to precisely localize vehicles and robots respectively. However, this method does not work when trees or tall buildings obstruct the satellite view.…”

Section: Related Workmentioning

confidence: 99%

Light-weight localization for vehicles using road markings

Ranganathan

Ilstrup

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Traditional vision-based localization methods such as visual SLAM suffer from practical problems in outdoor environments such as unstable feature detection and inability to perform location recognition under lighting, perspective, weather and appearance change. Additionally map construction on a large scale in these systems presents its own challenges. In this work, we present a novel method for precisely localizing vehicles on the road using signs marked on the road (road markings), which have the advantage of being distinct and easy to detect, their detection being robust under changes in lighting and weather. Our method uses corners detected on road markings to perform localization in global coordinates. The method consists of two phases -a mapping phase when a high-quality GPS device is used to automatically survey road marks and add them to a light-weight "map" or database, and a localization phase where road mark detection and look-up in the map, combined with visual odometry, produces precise localization. We present experiments using a real-time implementation operating in a car that demonstrates the improved localization robustness and accuracy of our system even when using road marks alone. However, in this case the trajectory between road marks has to be filled-in by visual odometry, which contributes drift. Hence, we also present a mechanism for combining roadmark-based maps with sparse feature-based maps that results in greater accuracy still. We see our use of road marks as a significant step in the general trend of using higher-level features for improved localization performance irrespective of environment conditions. 978-1-4673-6358-7/13/$31.00 ©2013 IEEE

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“…With the growing maturity of unmanned aerial vehicle (UAV) technology, UAVs are now used in various military and civilian fields, including intelligence reconnaissance, military strikes, search and rescue, land surveying and mapping, precision agriculture, and environmental monitoring [1][2][3][4]. Similar to other types of robots, accurate localization of their position is the prerequisite for UAVs to perform tasks smoothly [5]. Traditional navigation technology relies on GNSS; however, it has disadvantages, such as instability and susceptibility to interference [6].…”

Section: Introductionmentioning

confidence: 99%