2016
DOI: 10.3390/s16081268
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Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area

Abstract: Tall buildings are concentrated in urban areas. The outer walls of buildings are vertically erected to the ground and almost flat. Therefore, the vertical corners that meet the vertical planes are present everywhere in urban areas. These corners act as convenient landmarks, which can be extracted by using the light detection and ranging (LIDAR) sensor. A vertical corner feature based precise vehicle localization method is proposed in this paper and implemented using 3D LIDAR (Velodyne HDL-32E). The vehicle mot… Show more

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Cited by 47 publications
(32 citation statements)
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“…In Equation 7, H is the observation matrix for the measurement update, where the observation matrix is derived using the Jacobian calculated by the partial differentiation of all measurements into the state. As described in [8], for the observability of the measurement update, the state estimation can be performed even with only one feature point. In this study, in addition to the feature point, the error correction of the vehicle derived by the point-to-probability distribution scan matching was obtained.…”
Section: Extended Kalman Filter (Ekf) Configurationmentioning
confidence: 99%
“…In Equation 7, H is the observation matrix for the measurement update, where the observation matrix is derived using the Jacobian calculated by the partial differentiation of all measurements into the state. As described in [8], for the observability of the measurement update, the state estimation can be performed even with only one feature point. In this study, in addition to the feature point, the error correction of the vehicle derived by the point-to-probability distribution scan matching was obtained.…”
Section: Extended Kalman Filter (Ekf) Configurationmentioning
confidence: 99%
“…Although traffic signs occur less frequently in urban scenarios compared to other types of road furniture like road markings or street lamp poles, they offer the advantage of not only encoding a position, but also an unambiguous ID. Finally, Im et al [25] explore urban localization based on vertical corner features, which appear at the corners of buildings, in monocular camera images and lidar scans.…”
Section: Related Workmentioning
confidence: 99%
“…Therefore, the feature extraction of static objects is usually needed during the process of map construction and real-time registration. There are two main methods that may be considered; one method involves the application of machine learning methods for the recognition of moving and static objects [12], whilst the other involves the static object detection by extracting features such as the vertical corner features of buildings [13], and line features of curbs [14] and road lanes [15], respectively. However, in urban areas where moving objects are crowded and without a prior map, large amounts of occlusions between objects and more sparse point clouds (e.g., using LiDAR with fewer beams or detecting objects at longer distances) will make both recognition and feature detection difficult.…”
Section: Related Workmentioning
confidence: 99%