Automatic one step extrinsic calibration of a multi layer laser scanner relative to a stereo camera

Lisca, Gheorghe; Jeong, Pangyu; Nedevschi, Sergiu

doi:10.1109/iccp.2010.5606434

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…Other approaches require the user to select key points in laser data in order to perform the calibration [3]. A more similar solution to the one proposed in this paper can be found in Lisca, Pangyu and Nedevshi [4], where the calibration between a multi-line LRF and a stereo camera is obtained from the information of a known calibration object in the scene. This paper focuses on 3D/2D laser calibration for the AtlasCar project, which is a prototype for research on Advanced Driver Assistance Systems [5].…”

Section: Introductionmentioning

confidence: 93%

3D-2D Laser Range Finder Calibration Using a Conic Based Geometry Shape

Almeida

Dias

Oliveira

et al. 2012

Lecture Notes in Computer Science

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Abstract. The AtlasCar is a prototype that is being developed at the University of Aveiro to research advanced driver assistance systems. The car is equipped with several sensors: 3D and 2D laser scanners, a stereo camera, inertial sensors and GPS. The combination of all these sensor data in useful representations is essential. Therefore, calibration is one of the first problems to tackle. This paper focuses on 3D/2D laser calibration. The proposed method uses a 3D Laser Range Finder (LRF) to produce a reference 3D point cloud containing a known calibration object. Manual input from the user and knowledge of the object geometry are used to register the 3D point cloud with the 2D Lasers. Experimental results with simulated and real data demonstrate the effectiveness of the proposed calibration method.

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Section: Introductionmentioning

confidence: 93%

3D-2D Laser Range Finder Calibration Using a Conic Based Geometry Shape

Almeida

Dias

Oliveira

et al. 2012

Lecture Notes in Computer Science

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“…Other approaches such as (Li et al, 2011) and (Kwak et al, 2011) projects the features into a 2D plane to minimize the distance among the features in the different sensors. (Lisca et al, 2010) presents a CAD model based calibration system for inter-sensor matching. Similar approach based on triangular model is presented in (Debattisti et al, 2013) and based on circular models in (Fremont and Bonnifait, 2008).…”

Section: Related Workmentioning

confidence: 99%

Automatic Obstacle Classification using Laser and Camera Fusion

Ponz¹,

Rodríguez-Garavito²,

García³

et al. 2015

Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems

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State of the art Driving Assistance Systems and Autonomous Driving applications are employing sensor fusion in order to achieve trustable obstacle detection and classification under any meteorological and illumination condition. Fusion between laser and camera is widely used in ADAS applications in order to overcome the difficulties and limitations inherent to each of the sensors. In the system presented, some novel techniques for automatic and unattended data alignment are used and laser point clouds are exploited using Artificial Intelligence techniques to improve the reliability of the obstacle classification. New approaches to the problem of clustering sparse point clouds have been adopted, maximizing the information obtained from low resolution lasers. After improving cluster detection, AI techniques have been used to classify the obstacle not only with vision, but also with laser information. The fusion of the information acquired from both sensors, adding the classification capabilities of the laser, improves the reliability of the system.

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“…Among the documented works, there are generally two types of calibration: 1) calibrating a LIDAR to a reference frame defined by such as a camera [11], [12], [13] or the egovehicles odometry center [14] or 2) finding the relative geometries among different LIDARs [15], [16]. These calibration methods usually require some modification of the scene by introducing landmarks that are visible (or detectable) by LIDARs, such as specifically placed reflective targets [15], poles [17], checker boards [18], which introduce laborious * M. He field work, or rely on special facilities [16].…”

Section: Introductionmentioning

confidence: 99%

Calibration method for multiple 2D LIDARs system

Zhao

Cui

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Many robotic and mobile mapping systems have been developed using multiple 2D LIDARs (briefly multi-LIDAR system) to sense environment. In such systems, extrinsic calibration of all LIDARs is essential for making collaborative use of the data from different sensors. This research aims at developing a calibration method for multi-LIDAR systems at the general scene, such as an outdoor place or an underground parking-lot, without modification to environment by putting calibration targets. In this paper, the calibration method is proposed by aligning the 3D data of different LIDARs. They are concerned at two-levels: 1) reference calibration, i.e. finding the transformation from a reference LIDAR to the platform frame; 2) multi-LIDAR calibration, i.e. finding the LIDARs' relative geometries by referring to the reference one. The method is examined in calibrating the multiple 2D LIDARs on an intelligent vehicle platform POSS-V, where the data collected through a driving in an underground parking-lot are registered to find sensors' geometry. Calibration accuracy is examined by comparing with a CAD model of the scene, which was measured by using a total station.

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Automatic one step extrinsic calibration of a multi layer laser scanner relative to a stereo camera

Cited by 8 publications

References 12 publications

3D-2D Laser Range Finder Calibration Using a Conic Based Geometry Shape

3D-2D Laser Range Finder Calibration Using a Conic Based Geometry Shape

Automatic Obstacle Classification using Laser and Camera Fusion

Calibration method for multiple 2D LIDARs system

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