SLAM(Simultaneous Localization and Mapping),即同时定位与地图构建,目前被广泛应用于机器人领域。SLAM算法使得机器人处于陌生环境时,能够通过自身搭载的传感器来感知环境信息并建立环境地图,并完成对自身位姿的计算,从而能够在未知...SLAM(Simultaneous Localization and Mapping),即同时定位与地图构建,目前被广泛应用于机器人领域。SLAM算法使得机器人处于陌生环境时,能够通过自身搭载的传感器来感知环境信息并建立环境地图,并完成对自身位姿的计算,从而能够在未知环境中进行移动。随着研究者们对SLAM问题的深入研究,SLAM领域相关成果已非常丰富,但是有关室内场景SLAM的论述还不够系统。通过对现有的关于SLAM算法发展成果的总结和对比,对室内SLAM进行了综合性的阐述。首先介绍了SLAM的技术现状和室内场景SLAM在不同传感器下的分类问题;其次介绍了SLAM的经典框架;然后根据相关传感器种类的不同,简要介绍了不同传感器下常见的SLAM算法的原理,同时讨论了传统室内SLAM算法中存在的诸多局限性问题,引出了基于多传感器融合技术的SLAM和基于深度学习技术的SLAM两个研究方向;最后介绍了SLAM的未来发展趋势和应用领域。展开更多
基于三维点云的同时定位与建图(simultaneous localization and mapping, SLAM)是机器人导航与定位领域重要的技术之一.然而具有回环检测功能的三维点云SLAM系统仍鲜见于文献中.本文首先提出了一种新的基于三维点云的室外SLAM系统的框架...基于三维点云的同时定位与建图(simultaneous localization and mapping, SLAM)是机器人导航与定位领域重要的技术之一.然而具有回环检测功能的三维点云SLAM系统仍鲜见于文献中.本文首先提出了一种新的基于三维点云的室外SLAM系统的框架,该框架由里程计、回环检测、位姿优化3部分组成.其次针对回环检测,提出一种基于点云片段匹配约束的方法提升回环检测的效率.最后针对位姿优化,提出两种轨迹漂移优化算法,分别为全局一致性的回环调整算法和位姿预测和补偿算法.通过广泛的实验验证本文提出的方法,结果表明本文所提出的SLAM系统具有稳定和精确的位姿估计能力.展开更多
光伏电站具有场地尺度大、场景中结构稀少、阵列排布光伏组件形成狭长过道的环境特点。针对搭载2D激光雷达的巡检机器人使用simultaneous localization and mapping (SLAM)算法在光伏电站场景中进行定位和地图构建时出现位姿估计不准确...光伏电站具有场地尺度大、场景中结构稀少、阵列排布光伏组件形成狭长过道的环境特点。针对搭载2D激光雷达的巡检机器人使用simultaneous localization and mapping (SLAM)算法在光伏电站场景中进行定位和地图构建时出现位姿估计不准确、地图显示不完整的问题,以Cartographer算法为框架,提出一种基于因子图优化的前端优化策略。通过预积分处理构建惯性测量单元(IMU)因子,联合激光雷达数据扫描匹配后位姿因子,共同作为约束加入因子图中进行优化,获得更准确的估计位姿,并将此位姿嵌入原始算法进行地图构建。搭建模拟光伏电站环境和模拟狭长过道环境,对主流滤波算法、Cartographer算法和改进后算法进行对比实验,结果表明改进后算法所构建的地图尺寸精度更高,整体描述更准确。展开更多
This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift.First,a monitoring algorithm...This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift.First,a monitoring algorithm that can continuously detect the miscalibration in each frame is designed,leveraging the rotational motion each individual sensor observes.Then,as sensor drift occurs,the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion,which is further utilized to align the drifted LiDAR scan with the camera image.Finally,the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift,then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.展开更多
The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results ...The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.展开更多
文摘SLAM(Simultaneous Localization and Mapping),即同时定位与地图构建,目前被广泛应用于机器人领域。SLAM算法使得机器人处于陌生环境时,能够通过自身搭载的传感器来感知环境信息并建立环境地图,并完成对自身位姿的计算,从而能够在未知环境中进行移动。随着研究者们对SLAM问题的深入研究,SLAM领域相关成果已非常丰富,但是有关室内场景SLAM的论述还不够系统。通过对现有的关于SLAM算法发展成果的总结和对比,对室内SLAM进行了综合性的阐述。首先介绍了SLAM的技术现状和室内场景SLAM在不同传感器下的分类问题;其次介绍了SLAM的经典框架;然后根据相关传感器种类的不同,简要介绍了不同传感器下常见的SLAM算法的原理,同时讨论了传统室内SLAM算法中存在的诸多局限性问题,引出了基于多传感器融合技术的SLAM和基于深度学习技术的SLAM两个研究方向;最后介绍了SLAM的未来发展趋势和应用领域。
文摘基于三维点云的同时定位与建图(simultaneous localization and mapping, SLAM)是机器人导航与定位领域重要的技术之一.然而具有回环检测功能的三维点云SLAM系统仍鲜见于文献中.本文首先提出了一种新的基于三维点云的室外SLAM系统的框架,该框架由里程计、回环检测、位姿优化3部分组成.其次针对回环检测,提出一种基于点云片段匹配约束的方法提升回环检测的效率.最后针对位姿优化,提出两种轨迹漂移优化算法,分别为全局一致性的回环调整算法和位姿预测和补偿算法.通过广泛的实验验证本文提出的方法,结果表明本文所提出的SLAM系统具有稳定和精确的位姿估计能力.
文摘光伏电站具有场地尺度大、场景中结构稀少、阵列排布光伏组件形成狭长过道的环境特点。针对搭载2D激光雷达的巡检机器人使用simultaneous localization and mapping (SLAM)算法在光伏电站场景中进行定位和地图构建时出现位姿估计不准确、地图显示不完整的问题,以Cartographer算法为框架,提出一种基于因子图优化的前端优化策略。通过预积分处理构建惯性测量单元(IMU)因子,联合激光雷达数据扫描匹配后位姿因子,共同作为约束加入因子图中进行优化,获得更准确的估计位姿,并将此位姿嵌入原始算法进行地图构建。搭建模拟光伏电站环境和模拟狭长过道环境,对主流滤波算法、Cartographer算法和改进后算法进行对比实验,结果表明改进后算法所构建的地图尺寸精度更高,整体描述更准确。
基金Supported by National Natural Science Foundation of China(Grant Nos.52025121,52394263)National Key R&D Plan of China(Grant No.2023YFD2000301).
文摘This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift.First,a monitoring algorithm that can continuously detect the miscalibration in each frame is designed,leveraging the rotational motion each individual sensor observes.Then,as sensor drift occurs,the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion,which is further utilized to align the drifted LiDAR scan with the camera image.Finally,the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift,then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.
基金the National Key R&D Program of China(2018AAA0103103).
文摘The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.
