An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair

Shang, Erke; An, Xiangjing; Shi, Minjia; Meng, Deyuan; Li, Jian; Wu, Tao

doi:10.1007/s10846-014-0080-3

Cited by 16 publications

(8 citation statements)

References 14 publications

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“…26 is employed in our approach, which is mainly used to calibrate both intrinsic parameter and extrinsic parameter of the camera to achieve the equation (3). Calibration methods for LiDARs are also widely researched, [27][28][29] and we expand our previous work 30 to the implementation of the calibration in this application. The proposed algorithm is described as in Algorithm 1.…”

Section: Approach Descriptionmentioning

confidence: 99%

A fast calibration approach for onboard LiDAR-camera systems

Shang¹,

Dai²,

Nie³

et al. 2020

International Journal of Advanced Robotic Systems

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Outdoor surveillance and security robots have a wide range of industrial, military, and civilian applications. In order to achieve autonomous navigation, the LiDAR-camera system is widely applied by outdoor surveillance and security robots. The calibration of the LiDAR-camera system is essential and important for robots to correctly acquire the scene information. This article proposes a fast calibration approach that is different from traditional calibration algorithms. The proposed approach combines two independent calibration processes, which are the calibration of LiDAR and the camera to robot platform, so as to address the relationship between LiDAR sensor and camera sensor. A novel approach to calibrate LiDAR to robot platform is applied to improve accuracy and robustness. A series of indoor experiments are carried out and the results show that the proposed approach is effective and efficient. At last, it is applied to our own outdoor security robot platform to detect both positive and negative obstacles in a field environment, in which two Velodyne-HDL-32 LiDARs and a color camera are employed. The real application illustrates the robustness performance of the proposed approach.

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Section: Approach Descriptionmentioning

confidence: 99%

A fast calibration approach for onboard LiDAR-camera systems

Shang¹,

Dai²,

Nie³

et al. 2020

International Journal of Advanced Robotic Systems

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“…32 Then, the position of each scan point in vehicle coordinate can be computed. So, the distribution of scan points can be analyzed:…”

Section: Deduced Mathematical Model To Simulate a Single Scan Linementioning

confidence: 99%

“…When LiDAR is fixed under the new setup, its coordinate should be calibrated into the vehicle coordinate first. 32 Then, the position of each scan point in vehicle coordinate can be computed. So, the distribution of scan points can be analyzed:…”

Section: Deduced Mathematical Model To Simulate a Single Scan Linementioning

confidence: 99%

A feature matching and fusion-based positive obstacle detection algorithm for field autonomous land vehicles

Cui

et al. 2017

International Journal of Advanced Robotic Systems

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Positive obstacles will cause damage to field robotics during traveling in field. Field autonomous land vehicle is a typical field robotic. This article presents a feature matching and fusion-based algorithm to detect obstacles using LiDARs for field autonomous land vehicles. There are three main contributions: (1) A novel setup method of compact LiDAR is introduced. This method improved the LiDAR data density and reduced the blind region of the LiDAR sensor. (2) A mathematical model is deduced under this new setup method. The ideal scan line is generated by using the deduced mathematical model. (3) Based on the proposed mathematical model, a feature matching and fusion (FMAF)-based algorithm is presented in this article, which is employed to detect obstacles. Experimental results show that the performance of the proposed algorithm is robust and stable, and the computing time is reduced by an order of two magnitudes by comparing with other exited algorithms. This algorithm has been perfectly applied to our autonomous land vehicle, which has won the champion in the challenge of Chinese "Overcome Danger 2014" ground unmanned vehicle.

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“…When the LiDAR is mounted under the proposed setup and calibrated onto the vehicle coordinate frame (the calibration approach can be found in our previous work (Shang et al., )), the scan point distribution is shown as follows: The angle between every two adjacent points is a fixed angle

θ_{i}

(Figure ), which can be figured out by equation .In Figure ,

θ_{i}^{'}

depends on the number M when the scan line works in a circle. Thus,

θ_{i}^{'} = \frac{360^{\circ}}{M}

, where M is a fixed parameter.…”

Section: The Mathematical Model Of the Proposed Setupmentioning

confidence: 99%

LiDAR Based Negative Obstacle Detection for Field Autonomous Land Vehicles

Shang

et al. 2015

J. Field Robotics

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Negative obstacles for field autonomous land vehicles (ALVs) refer to ditches, pits, or terrain with a negative slope, which will bring risks to vehicles in travel. This paper presents a feature fusion based algorithm (FFA) for negative obstacle detection with LiDAR sensors. The main contributions of this paper are fourfold: (1) A novel three‐dimensional (3‐D) LiDAR setup is presented. With this setup, the blind area around the vehicle is greatly reduced, and the density of LiDAR data is greatly improved, which are critical for ALVs. (2) On the basis of the proposed setup, a mathematical model of the point distribution of a single scan line is deduced, which is used to generate ideal scan lines. (3) With the mathematical model, an adaptive matching filter based algorithm (AMFA) is presented to implement negative obstacle detection. Features of simulated obstacles in each scan line are employed to detect the real negative obstacles. They are supposed to match with features of the potential real obstacles. (4) Grounded on AMFA algorithm, a feature fusion based algorithm is proposed. FFA algorithm fuses all the features generated by different LiDARs or captured at different frames. Bayesian rule is adopted to estimate the weight of each feature. Experimental results show that the performance of the proposed algorithm is robust and stable. Compared with the state‐of‐the‐art techniques, the detection range is improved by 20%, and the computing time is reduced by an order of two magnitudes. The proposed algorithm had been successfully applied on two ALVs, which won the champion and the runner‐up in the “Overcome Danger 2014” ground unmanned vehicle challenge of China.

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An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair

Cited by 16 publications

References 14 publications

A fast calibration approach for onboard LiDAR-camera systems

A fast calibration approach for onboard LiDAR-camera systems

A feature matching and fusion-based positive obstacle detection algorithm for field autonomous land vehicles

LiDAR Based Negative Obstacle Detection for Field Autonomous Land Vehicles

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