Laser-based obstacle avoidance algorithm for four driving/steering wheels autonomous vehicle

Dumitrascu, Bogdan; Filipescu, Adrian; Petrea, George; Filipescu, Silviu; Minca, Eugénia; Voda, Alina

doi:10.1109/icstcc.2013.6688958

Cited by 6 publications

(1 citation statement)

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“…Light detection and ranging (LIDAR) is a substantial contributing sensor for autonomous driving [ 1 , 2 ], 3D reconstruction [ 3 , 4 , 5 ], simultaneous localization and mapping (SLAM) [ 6 , 7 , 8 , 9 , 10 ] and visual navigation [ 11 , 12 , 13 , 14 ], etc. With the benefits of high accuracy and ignoring of background illumination, the rotating two-dimensional light detection and ranging (R2D-LIDAR) becomes an attractive sensor for outdoor mobile robots [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Calibration Method for a Rotating 2D LIDAR System

Zeng

Dai

et al. 2018

Sensors

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This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used in the field of robotics owing to its low cost and dense scanning data. Nevertheless, the R2D-LIDAR system must be calibrated before building the geometric model because there are assembled deviation and abrasion between the 2D LIDAR and the rotating unit. Hence, the calibration procedures should contain both the adjustment between the two devices and the bias of 2D LIDAR itself. The main purpose of this work is to resolve the 2D LIDAR bias issue with a flat plane based on the Levenberg–Marquardt (LM) algorithm. Experimental results for the calibration of the R2D-LIDAR system prove the reliability of this strategy to accurately estimate sensor offsets with the error range from −15 mm to 15 mm for the performance of capturing scans.

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