Extrinsic 6DoF calibration of a radar–LiDAR–camera system enhanced by radar cross section estimates evaluation

Peršić, Juraj; Marković, Ivan

doi:10.1016/j.robot.2018.11.023

Cited by 53 publications

(46 citation statements)

References 21 publications

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“…The studies of extrinsic calibration and the methodologies are well-established in the literature, see reference [ 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 ] for example. Though, the extrinsic calibration of multiple sensors with various physical measurement principles can pose a challenge in multi-sensor systems.…”

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

“…For instance, it is often challenging to match the corresponding features between camera images (dense data in pixels) and 3D LiDAR or radar point clouds (sparse depth data without color information) [ 144 ]. The target-based extrinsic calibration approach employs specially designed calibration targets, such as marker-less planar pattern [ 51 ], checkerboard pattern [ 145 ], orthogonal and trihedral reflector [ 51 , 143 , 146 , 148 ], circular pattern to calibrate multiple sensor modalities in autonomous systems. The targetless extrinsic calibration approach leverages the estimated motion by individual sensors or utilizes the features in the perceiving environment to calibrate the sensors.…”

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

“…As highlighted in the previous paragraph, each sensing modality has a different physical measurement principle; thus, sensor setups with more modalities may duplicate the calibration efforts, especially in mobile robots in which sensors are frequently dismounted or repositioned. For this reason, reference [ 145 , 148 ] presented a novel calibration method to extrinsically calibrate all three sensing modalities, namely radar, LiDAR, and camera with a specially designed calibration target. Table 5 below summarizes the open-source extrinsic sensor calibration tools, specifically for camera, LiDAR sensor, and radar sensor extrinsic calibration.…”

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

487

133

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With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

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Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

487

133

View full text Add to dashboard Cite

show abstract

“…The studies of extrinsic calibration and the methodologies are well-established in the literature, see reference [141][142][143][144][145][146][147][148][149] for example. However, the extrinsic calibration of multiple sensors with different physical measurement principles can pose a challenge in multi-sensor systems.…”

Section: Extrinsic Calibration Overviewmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong¹,

Velasco-Hernandez²,

Barry³

et al. 2021

Preprint

148

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The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

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“…Some existing calibration methods have been proposed for different sensors; for cameras in [38,39], for lidar and camera in [40][41][42], for laser scanner and camera in [43][44][45][46], for radar and camera in [63][64][65], and for radar, lidar, and camera in [66,67].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vehicle Trailer Rotation Using Calibrated Radars With Multiple Viewpoints

Olutomilayo¹

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Extrinsic 6DoF calibration of a radar–LiDAR–camera system enhanced by radar cross section estimates evaluation

Cited by 53 publications

References 21 publications

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Estimation of Vehicle Trailer Rotation Using Calibrated Radars With Multiple Viewpoints

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