Generating 3D fundamental map by large-scale SLAM and graph-based optimization focused on road center line

Niijima, Shun; Nitta, Jirou; Sasaki, Yoko; Mizoguchi, Hiroshi

doi:10.1109/roman.2017.8172455

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…The road network of publicly available maps has often been incorporated into SLAM for autonomous vehicles running on the road [15], [16]. The OpenStreetSLAM [17] uses publicly available maps to improve the accumulated error of visual odometry (VO) algorithms.…”

Section: Related Workmentioning

confidence: 99%

Semi-Automatic Town-Scale 3D Mapping Using Building Information From Publicly Available Maps

et al. 2022

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Section: Related Workmentioning

confidence: 99%

Semi-Automatic Town-Scale 3D Mapping Using Building Information From Publicly Available Maps

et al. 2022

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“…Zhou et al [27] proposed a method to align and extract pavements, boundaries, lane markings through point cloud data. Niijima et al [28] further applied road centerline estimation with pose graph optimization to reconstruct the point cloud on a large scale. By adding a more trustable road centerline reference, the error of the point cloud can be minimized.…”

Section: Related Workmentioning

confidence: 99%

Mobile Mapping System for Automatic Extraction of Geodetic Coordinates for Traffic Signs Based on Enhanced Point Cloud Reconstruction

2022

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Lidar sensors are commonly equipped on a mobile mapping system (MMS) to establish point clouds for HD map creation. However, the point clouds themselves do not contain object attributes. Therefore, human operators have to manually obtain objects' position to assign attributes for further HD map conversion, inevitably resulting in time-consuming processes and significant labor costs. To solve the above problems, in this paper, we present an MMS equipped with non-survey grade Lidar, commercial grade camera, and entry level GNSS/INS, which incorporates ground control points (GCPs) with a Normal Distribution Transform Simultaneously Localization and Mapping (NDT SLAM) refinement and fluctuation adjustment to secure both absolute position accuracy and relative position accuracy of the reconstructed point cloud. Meanwhile, a deep neural network for image detection is employed to obtain the bounding box of traffic signs from each image frame. By applying the translation and rotation transformation between Lidar points and camera pixels, intersection of the detected object in the image and Lidar scan points can be found. By accumulating extracted Lidar points of the traffic sign in several detection frames, we can then obtain an accurate 3D geodetic coordinate of the traffic signs. Experimental results show that point clouds can be reconstructed with an average 3D RMSE of only 8.6cm, and center geodetic coordinates of traffic signs can be further extracted in sub-meter accuracy to significantly reduce labor work in HD map creation.INDEX TERMS HD map, point cloud, GIS, autonomous driving.

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“…Lidar point clouds have been widely discussed for extracting road features [ 8 , 9 ] in various manners. Jeong [ 8 ] presented an intensity correction flow to enhance the feature intensity due to the distance from the moving platform and the angle of incidence.…”

Section: Related Workmentioning

confidence: 99%

“…Jeong [ 8 ] presented an intensity correction flow to enhance the feature intensity due to the distance from the moving platform and the angle of incidence. Niijima [ 9 ] presented a method combining 3D point clouds with fundamental geospatial data information, because aligning road features with a graph-based map could add stability to overcome the errors caused by the feature extraction process. Nagy [ 10 ] proposed a voxel-based convolutional neural network (CNN) architecture which has the ability to remove the impact of the phantom phenomenon for point clouds collected on the open roads.…”

Section: Related Workmentioning

confidence: 99%

Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction

Cheng

Hsu

Wang

2020

Sensors

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Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles. Among the major processes for color point cloud reconstruction, we first synchronize the timestamps of each sensor. The calibration process between camera and Lidar is developed to obtain the translation and rotation matrices, based on which color attributes can be composed into the corresponding Lidar points. We also employ control points to adjust the point cloud for fine tuning the absolute position. To overcome the limitation of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning system, we utilize Normal Distribution Transform (NDT) localization to refine the trajectory to solve the multi-scan dispersion issue. Experimental results show that the color point cloud reconstructed by the proposed MMS has a position error in centimeter-level accuracy, meeting the requirement of high definition (HD) maps for autonomous driving usage.

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Generating 3D fundamental map by large-scale SLAM and graph-based optimization focused on road center line

Cited by 8 publications

References 11 publications

Semi-Automatic Town-Scale 3D Mapping Using Building Information From Publicly Available Maps

Semi-Automatic Town-Scale 3D Mapping Using Building Information From Publicly Available Maps

Mobile Mapping System for Automatic Extraction of Geodetic Coordinates for Traffic Signs Based on Enhanced Point Cloud Reconstruction

Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction

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