LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as ...LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction.Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks.The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems.Recently,extensive studies have been conducted on the calibration of extrinsic parameters.Although several calibration methods facilitate sensor fusion,a comprehensive summary for researchers and,especially,non-expert users is lacking.Thus,we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design.Based on the calibration information sources,this study classifies these methods as target-based or targetless.For each type of calibration method,further classification was performed according to the diverse types of features or constraints used in the calibration process,and their detailed implementations and key characteristics were introduced.Thereafter,calibration-accuracy evaluation methods are presented.Finally,we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.展开更多
Minor errors in the spoil deposition process,such as placing stronger materials with higher shear strength over weaker ones,can lead to potential dump failure.Irregular deposition and inadequate compaction complicate ...Minor errors in the spoil deposition process,such as placing stronger materials with higher shear strength over weaker ones,can lead to potential dump failure.Irregular deposition and inadequate compaction complicate coal spoil behaviour,neces-sitating a robust methodology for temporal monitoring.This study explores using unmanned aerial vehicles(UAV)equipped with red-green-blue(RGB)sensors for efficient data acquisition.Despite their prevalence,raw UAV data exhibit temporal inconsistency,hindering accurate assessments of changes over time which could be attributed to radiometric errors.To this end,the study introduces an empirical line calibration with invariant targets(ELC-IT),for precise calibration across diverse scenes,particularly in the context of UAV imagery used to monitor the evolving nature of spoil dumps.To evaluate the effec-tiveness of this calibration approach,accuracy assessment of an object-based classification is conducted on both calibrated and uncalibrated data.This classification involves several steps:performing segmentation,carrying out feature extraction,and integrating the extracted features and ground truth labels collected over the time period of UAV image capture into machine learning pipelines.Calibrated RGB data exhibit a substantial performance advantage,achieving a 90.7%overall accuracy for spoil pile classification using ensemble(subspace discriminant),representing a noteworthy 7%improvement compared to classifying uncalibrated data.The study highlights the critical role of data calibration in optimising UAV effectiveness for spatio-temporal mine dump monitoring.These findings play a crucial role in informing and refining sustainable management practices within the domain of mine waste management.展开更多
基金Supported by Beijing Natural Science Foundation(Grant No.L241012)the National Natural Science Foundation of China(Grant No.62572468).
文摘LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction.Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks.The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems.Recently,extensive studies have been conducted on the calibration of extrinsic parameters.Although several calibration methods facilitate sensor fusion,a comprehensive summary for researchers and,especially,non-expert users is lacking.Thus,we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design.Based on the calibration information sources,this study classifies these methods as target-based or targetless.For each type of calibration method,further classification was performed according to the diverse types of features or constraints used in the calibration process,and their detailed implementations and key characteristics were introduced.Thereafter,calibration-accuracy evaluation methods are presented.Finally,we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.
基金supported by the Australian Coal Industry's Research Program(ACARP)(C29048).
文摘Minor errors in the spoil deposition process,such as placing stronger materials with higher shear strength over weaker ones,can lead to potential dump failure.Irregular deposition and inadequate compaction complicate coal spoil behaviour,neces-sitating a robust methodology for temporal monitoring.This study explores using unmanned aerial vehicles(UAV)equipped with red-green-blue(RGB)sensors for efficient data acquisition.Despite their prevalence,raw UAV data exhibit temporal inconsistency,hindering accurate assessments of changes over time which could be attributed to radiometric errors.To this end,the study introduces an empirical line calibration with invariant targets(ELC-IT),for precise calibration across diverse scenes,particularly in the context of UAV imagery used to monitor the evolving nature of spoil dumps.To evaluate the effec-tiveness of this calibration approach,accuracy assessment of an object-based classification is conducted on both calibrated and uncalibrated data.This classification involves several steps:performing segmentation,carrying out feature extraction,and integrating the extracted features and ground truth labels collected over the time period of UAV image capture into machine learning pipelines.Calibrated RGB data exhibit a substantial performance advantage,achieving a 90.7%overall accuracy for spoil pile classification using ensemble(subspace discriminant),representing a noteworthy 7%improvement compared to classifying uncalibrated data.The study highlights the critical role of data calibration in optimising UAV effectiveness for spatio-temporal mine dump monitoring.These findings play a crucial role in informing and refining sustainable management practices within the domain of mine waste management.
