Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area

Im, Jun-Hyuck; Im, Sung-Hyuck; Jee, Gyu-In

doi:10.3390/s16081268

Cited by 47 publications

(32 citation statements)

References 23 publications

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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%

Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas

Kim

Jee

2020

Sensors

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We propose a free-resolution probability distributions map (FRPDM) and an FRPDM-based precise vehicle localization method using 3D light detection and ranging (LIDAR). An FRPDM is generated by Gaussian mixture modeling, based on road markings and vertical structure point cloud. Unlike single resolution or multi-resolution probability distribution maps, in the case of the FRPDM, the resolution is not fixed and the object can be represented by various sizes of probability distributions. Thus, the shape of the object can be represented efficiently. Therefore, the map size is very small (61 KB/km) because the object is effectively represented by a small number of probability distributions. Based on the generated FRPDM, point-to-probability distribution scan matching and feature-point matching were performed to obtain the measurements, and the position and heading of the vehicle were derived using an extended Kalman filter-based navigation filter. The experimental area is the Gangnam area of Seoul, South Korea, which has many buildings around the road. The root mean square (RMS) position errors for the lateral and longitudinal directions were 0.057 m and 0.178 m, respectively, and the RMS heading error was 0.281°.

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Section: Extended Kalman Filter (Ekf) Configurationmentioning

confidence: 99%

Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas

Kim

Jee

2020

Sensors

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“…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%

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Schaefer

Büscher

Vertens

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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Due to their ubiquity and long-term stability, polelike objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation [1].In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the system with localizing a mobile robot over the course of 15 months in an urban area based on an initial map, confronting it with constantly varying routes, differing weather conditions, seasonal changes, and construction sites. Second, we show that the proposed approach clearly outperforms a recently published method in terms of accuracy.

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“…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%

A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Wang

Sotelo

et al. 2019

Electronics

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The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

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Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area

Cited by 47 publications

References 23 publications

Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas

Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

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