Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction

Sensors
Cheng-Wei PengWei-Yen Wang

Abstract

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 ...Continue Reading

References

Jun 28, 2012·Sensors·Sergio MadeiraLuísa Bastos

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Citations

Feb 3, 2021·Sensors·Jordi PalacínEduard Clotet

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Rigel
Leica
Calibration Toolkit
Trimble
nVIDIA Driveworks
Optech
NMEA
OpenCV
Autoware
nVIDIA

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