Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks a...Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.展开更多
目前水果采摘存在劳动力短缺、采摘效率低和作业环境复杂等问题,亟须发展具备高精度感知与自主作业能力的智能化采摘装备,以全面提升果实采摘的效率和质量。传感器技术在水果采摘机器人中的应用包括路径规划、果实识别、定位及抓取控制...目前水果采摘存在劳动力短缺、采摘效率低和作业环境复杂等问题,亟须发展具备高精度感知与自主作业能力的智能化采摘装备,以全面提升果实采摘的效率和质量。传感器技术在水果采摘机器人中的应用包括路径规划、果实识别、定位及抓取控制等关键任务环节。针对非结构化果园环境,视觉、触觉与激光传感器的协同应用可实现目标识别、位置感知与避障控制,显著提升了采摘机器人对复杂环境的适应能力与作业精度,但是现有传感器仍然存在一些技术短板,如视觉传感器易受阳光干扰、枝叶遮挡和果实密集分布等因素影响,导致目标检测困难;触觉传感器易受温湿度影响,难以量化复杂的力学反馈,因而细微抓取力控制困难;由于非结构化环境下的路径优化困难,且激光传感器成本高昂,限制了其大规模应用。同时,单一传感器存在感知维度单一、环境适应性不足和果实特征感知不足等局限,难以适应非结构化果园环境。为此,针对多传感器融合技术面临的数据异构性、时序同步性和计算复杂性等挑战,对传感器技术在水果采摘机器人的未来应用进行了展望,指出融合红外、紫外等多波段成像技术和高动态范围(high dynamic range imaging,HDR)成像技术,柔性电子皮肤结合仿生结构设计的多传感器融合技术有望得到广泛应用。展开更多
针对视觉SLAM(simultaneous localization and mapping)算法在动态环境下容易出现重定位失败的问题,提出了一种基于自身运动约束的动态SLAM算法。采用YOLOv5s初步区分前景与背景特征点,仅利用背景特征点进行位姿初始化;利用IMU位姿信息...针对视觉SLAM(simultaneous localization and mapping)算法在动态环境下容易出现重定位失败的问题,提出了一种基于自身运动约束的动态SLAM算法。采用YOLOv5s初步区分前景与背景特征点,仅利用背景特征点进行位姿初始化;利用IMU位姿信息不受动态环境影响的特性,计算每个特征点的自身运动约束值;根据背景特征点的特征约束结果设计了动态概率模型,自适应确定当前帧特征点约束的动态阈值,去除动态特征点并更新相机位姿。使用仿真数据集和真实环境数据集进行了实验验证。实验结果表明,在仿真数据集中,该方法能去除沿极线运动的特征点,相较于Dyna-SLAM,在均方根误差和标准差两项指标的提升率为87.81%和83.17%,相较于AirDos提升率分别为51.62%和41.91%。真实动态环境中重定位能力强于Dyna-SLAM,较AirDos无明显精度提升但运行速度提升29.48%。展开更多
基金supported by the National Natural Science Foundationof China(61100207)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAK14B03)+1 种基金the Fundamental Research Funds for the Central Universities(2013PT132013XZ12)
文摘Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.
文摘目前水果采摘存在劳动力短缺、采摘效率低和作业环境复杂等问题,亟须发展具备高精度感知与自主作业能力的智能化采摘装备,以全面提升果实采摘的效率和质量。传感器技术在水果采摘机器人中的应用包括路径规划、果实识别、定位及抓取控制等关键任务环节。针对非结构化果园环境,视觉、触觉与激光传感器的协同应用可实现目标识别、位置感知与避障控制,显著提升了采摘机器人对复杂环境的适应能力与作业精度,但是现有传感器仍然存在一些技术短板,如视觉传感器易受阳光干扰、枝叶遮挡和果实密集分布等因素影响,导致目标检测困难;触觉传感器易受温湿度影响,难以量化复杂的力学反馈,因而细微抓取力控制困难;由于非结构化环境下的路径优化困难,且激光传感器成本高昂,限制了其大规模应用。同时,单一传感器存在感知维度单一、环境适应性不足和果实特征感知不足等局限,难以适应非结构化果园环境。为此,针对多传感器融合技术面临的数据异构性、时序同步性和计算复杂性等挑战,对传感器技术在水果采摘机器人的未来应用进行了展望,指出融合红外、紫外等多波段成像技术和高动态范围(high dynamic range imaging,HDR)成像技术,柔性电子皮肤结合仿生结构设计的多传感器融合技术有望得到广泛应用。
文摘针对视觉SLAM(simultaneous localization and mapping)算法在动态环境下容易出现重定位失败的问题,提出了一种基于自身运动约束的动态SLAM算法。采用YOLOv5s初步区分前景与背景特征点,仅利用背景特征点进行位姿初始化;利用IMU位姿信息不受动态环境影响的特性,计算每个特征点的自身运动约束值;根据背景特征点的特征约束结果设计了动态概率模型,自适应确定当前帧特征点约束的动态阈值,去除动态特征点并更新相机位姿。使用仿真数据集和真实环境数据集进行了实验验证。实验结果表明,在仿真数据集中,该方法能去除沿极线运动的特征点,相较于Dyna-SLAM,在均方根误差和标准差两项指标的提升率为87.81%和83.17%,相较于AirDos提升率分别为51.62%和41.91%。真实动态环境中重定位能力强于Dyna-SLAM,较AirDos无明显精度提升但运行速度提升29.48%。
