基于激光雷达SLAM的三维点云自适应算法

SLAM(Simultaneously Localization And Mapping)同步定位与地图构建作为移动机器人智能感知的关键技术。但是,大多已有的SLAM方法是在静止环境下实现的,当环境中存在移动频繁的障碍物时,SLAM建图会产生运动畸变,导致机器人无法进行精准的定位导航。同时,激光雷达等三维扫描设备获得的三维点云数据存在着大量的冗余三维数据点,过多的冗余数据不仅浪费大量的存储空间,同时也影响了各种点云处理算法的实时性。针对以上问题,本文提出一种SLAM运动畸变去除方法和一种基于曲率的点云数据分类简化框架。它通过激光插值法优化SLAM运动畸变,将优化后的点云数据分类简化。它能在提高SLAM建图精度,同时也很好的消除三维点云数据中特征不明显区域的冗余数据点,大大提高计算机运行效率。     

动态场景下基于视觉的SLAM技术研究

针对视觉同时定位与地图构建(SLAM)技术在动态环境中存在定位精度低、地图虚影等问题,提出了一种基于深度学习的动态SLAM算法。该算法利用网络参数少且目标识别率高的YOLOv8n改善系统的视觉前端,为视觉前端增加语义信息,提取动态区域特征点。然后采用LK光流法识别动态区域的动态特征点,剔除动态特征点并保留动态区域内的静态特征点,提高特征点利用率。此外,该算法通过增加地图构建线程,剔除YOLOv8n提取的动态物体点云,接收前端提取的语义信息,实现静态语义地图构建,消除由动态物体产生的虚影。实验结果显示,在动态环境下该算法与ORB-SLAM3相比,定位精度提升92.71%,与其他动态视觉SLAM算法相比,也有小幅度改善。     

动态场景下基于视觉同时定位与地图构建技术的多层次语义地图构建方法

为提高视觉同时定位与地图构建(SLAM)技术的环境适应性和语义信息理解能力,该文提出一种可以在动态场景下实现多层次语义地图构建的视觉SLAM方案。首先利用被迫移动物体与动态目标间的空间位置关系,并结合目标检测网络和光流约束判断真正的动态目标,从而剔除动态特征点;其次提出一种基于超体素的快速点云分割方案,将基于静态区域构建的3维地图进行优化,构建了物体级的点云语义地图;同时构建的语义地图可以提供更高精度的训练数据样本,进一步用来提升目标检测网络性能。在TUM和ICL-NUIM数据集上的实验结果表明,该方法在定位精度上远优于目前主流的动态场景下的视觉SLAM方案,证明了该方法在高动态场景中具有较好的稳定性和鲁棒性;在建图精度和质量上,经过将重建的不同种类地图与各个现有方法进行比较,验证了提出的多层次语义地图构建的方法在静态和高动态场景中的有效性与适用性。     

Onboard dynamic RGB-D simultaneous localization and mapping for mobile robot navigation

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.     

面向室内动态场景的VSLAM

传统VSLAM算法基于静态场景实现,其在室内动态场景下定位精度退化,三维稀疏点云地图也会出现动态特征点误匹配等问题.文中在ORB-SLAM2框架上进行改进,结合Mask R-CNN进行图像的语义分割,剔除位于动态物体上的动态特征点,优化了相机位姿,得到了静态的三维稀疏点云地图.在公开的TUM数据集上的实验结果表明,结合...     

基于双层蚁群算法和动态环境的机器人路径规划方法

针对动态环境未知时变的特点,提出一种机器人路径规划新方法.在该方法中,首先对栅格法建立的环境模型进行凸化处理,以避免机器人沿规划路径移动时陷入U型陷阱,从而加快路径规划的速度;其次,提出双层蚁群算法（DACO）,在每次迭代中先用外层蚁群算法寻找一条路径,然后以该路径为基础构造一个小环境,接着在该环境下用内层蚁群算法重新寻优,若寻得的路径质量更高,则更新路径并执行本文给出的一种新型信息素二次更新策略;最后,针对环境中不同动态障碍物的体积和速度,提出三种避障策略.动态环境下,机器人先由DACO算法规划一条静态环境下从起点到终点的全局最优路径,然后从当前起点开始,通过自带传感器获取动态环境信息,并根据需要执行等待、正碰或追尾避障策略,到达新的起点.仿真实验表明,该方法可以在动态环境下实时地为移动机器人规划出一条安全且最短的路径,是求解移动机器人路径规划问题的一种切实有效的方法.     

基于在线估计的视觉SLAM低光照图像增强算法

为了提升基于特征点的双目视觉定位算法在低光照环境下定位的准确性,提出一种基于在线估计的视觉同步定位与地图构建(simultaneous localization and mapping,SLAM)低光照图像增强算法.通过在线估计图像亮度值,实时更新图像增强算法的参数,解决了基于固定参数的图像增强算法在图像较亮、较暗等情况下的不适用性问题.首先,通过ORB-SLAM2系统寻找定位准确度的影响因素,并通过在线估计参数的方法实时更新相关参数.其次,利用低光照图像增强算法(low-light image enhancement,LIME)改善图像效果.最后,根据增强后的图像进行特征点提取,提升了特征匹配准确度,进而提升了定位的准确度.在公开EuRoC数据集上,通过与目前广泛使用的ORB-SLAM2算法进行对比实验,结果表明本文提出的视觉SLAM系统,具有更好的定位准确性及鲁棒性.     

Detecting moving object from dynamic background video sequences via simulating heat conduction

Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences.     

基于扩展卡尔曼滤波器的移动机器人SLAM研究

文中阐述一种移动机器人SLAM问题的解决方法,首先利用激光测距仪得到环境中障碍物的监测图表,然后增量的构建全局地图。利用扩展卡尔曼滤波器（EKF）创建移动机器人定位计算的有界估量;最后通过仿真和物理实验验证了该方法的正确性。可为解决机器人在未知环境下的地图创建与定位问题提供理论依据,具有实际意义。     

Moving object detection and tracking from video captured by moving camera

This paper presents an effective method for the detection and tracking of multiple moving objects from a video sequence captured by a moving camera without additional sensors. Moving object detection is relatively difficult for video captured by a moving camera, since camera motion and object motion are mixed. In the proposed method, the feature points in the frames are found and then classified as belonging to foreground or background features. Next, moving object regions are obtained using an integration scheme based on foreground feature points and foreground regions, which are obtained using an image difference scheme. Then, a compensation scheme based on the motion history of the continuous motion contours obtained from three consecutive frames is applied to increase the regions of moving objects. Moving objects are detected using a refinement scheme and a minimum bounding box. Finally, moving object tracking is achieved using a Kalman filter based on the center of gravity of a moving object region in the minimum bounding box. Experimental results show that the proposed method has good performance.     

Cooperation Issues and Distributed Sensing for Multirobot Systems

This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively. Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions. But the illustrated techniques can be usefully applied also to other service robotics fields like, for example, videosurveillance. Two issues are addressed in the paper. The former refers to the problem of dynamic role assignment in a team of robots. The latter concerns the problem of sharing the sensory information to cooperatively track moving objects. Both these problems have been extensively investigated over the past years by the MSL robot teams. In our paper, each individual robot has been designed to become reactively aware of the environment configuration. In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information. We present the successful performance of the Artisti Veneti robot team at the MSL Challenge competitions of RoboCup-2003 to show the effectiveness of our proposed hybrid architecture, as well as some tests run in laboratory to validate the omnidirectional distributed vision system which allows us to share the information gathered by the omnidirectional cameras of our robots.     

基于IMU/视觉融合的导航定位算法研究

针对基于视觉传感器的移动机器人在快速运动或发生旋转时出现图像模糊和特征丢失,以至无法进行特征匹配,从而导致系统定位和建图的准确度及精确度下降问题,该文提出了一种以深度相机(RGB_D)融合惯性测量单元（IMU）的方案。采用ORB SLAM2算法进行位姿估计,同时将IMU信息作为约束弥补相机数据的缺失。两种传感器的测量数据采用基于扩展卡尔曼滤波的松耦合方式进行非线性优化,通过数据采集实验表明,该方法能有效提高机器人的定位精度和系统建图效果。     

Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

Simultaneous localization and mapping (SLAM) technology becomes more and more important in robot localization. The purpose of this paper is to improve the robustness of visual features to lighting changes and increase the recall rate of map re-localization under different lighting environments by optimizing the image transformation model. An image transformation method based on matches and photometric error (name the method as MPT) is proposed in this paper, and it is seamlessly integrated into the pre-processing stage of the feature-based visual SLAM framework. The results of the experiment show that the MPT method has a better matching effect on different visual features. In addition, the image transformation module encapsulated by a robot operating system (ROS) can be used with multiple visual SLAM systems and improve its re-localization effect under different lighting environments.     

Safe Navigation of a Mobile Robot Considering Visibility of Environment

We present one approach to achieve safe navigation in an indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms possess fundamental limitations in that the robot can avoid only ldquovisiblerdquo ones among surrounded obstacles. In a real environment, it is not possible to detect all the dynamic obstacles around the robot. There are many occluded regions due to the limited field of view. In order to avoid collisions, it is desirable to exploit visibility information. This paper proposes a safe navigation scheme to reduce collision risk considering occluded dynamic obstacles. The robot's motion is controlled by the hybrid control scheme. The possibility of collision is dually reflected to path planning and speed control. The proposed scheme clearly indicates the structural procedure on how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The experimental results show that the robot moves along the safe path to obtain sufficient field of view. In addition, safe speed constraints are applied in motion control. It is experimentally verified that a robot safely navigates in dynamic indoor environment by adopting the proposed scheme.     

基于运动目标的De-Interlace算法

本文提出一种新颖的基于运动目标的De-interlace算法.该算法以实际的运动目标作为操作对象,引入一种较精确的运动目标提取方法,并采用免疫克隆选择算法加速匹配目标的搜索过程.新算法融合了运动补偿、中值滤波、Weave、Bob等De-interlace方法.与流行的基于运动块补偿的De-interlace算法相比,新算法更适应复杂的视频序列,不仅可以处理平移运动,还适用于旋转、尺度变换等复杂运动情况.实验结果表明新算法的整体性能优于基于块匹配的方法.     

Multi-hierarchy interaction control of a redundant robot using impedance learning

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body.     

Multi‐robot Mapping Using Omnidirectional‐Vision SLAM Based on Fisheye Images

This paper proposes a global mapping algorithm for multiple robots from an omnidirectional‐vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas–Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi‐robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional‐vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.     

视觉惯性里程计异常视觉测量的检测与处理

对于视觉惯性里程计(VIO),视觉遮挡、运动物体等复杂场景可能带来异常的视觉测量,导致系统定位精确度急剧下降。对此,提出了一种新的VIO异常视觉测量的检测和处理方法。通过选取检测指标、设置先验阈值和设计检测分类器,实现对异常视觉测量的检测与分类;提出多传感器融合策略和自适应误差加权算法,及时消除与实际运动不一致的异常视觉测量的影响;最后,将异常视觉测量检测和处理算法整合到基于关键帧的视觉惯性里程计(OKVIS)系统中,提出了视觉惯性里程计的异常检测和处理(EDS-VIO)系统框架。在复杂场景仿真数据集上的评测结果表明,EDS-VIO比OKVIS取得了更好的性能,定位误差均值从1.045 m下降到0.437 m。所提方法较好地提升了VIO在复杂场景中的定位精确度和鲁棒性。     

基于HSV空间阴影去除方法研究与应用

在智能视频监控系统中,运动阴影如果被误判为运动目标,将会影响到场景中运动目标的准确提取、跟踪和预测。针对这一问题,设计了一种基于HSV颜色空间的阴影去除方法。方法首先将背景差法和三帧差分法相结合,用于提取运动目标,再将提取的含有阴影的运动目标区域映射到其HSV色彩空间,通过与背景和相邻帧的亮度、饱和度比较,实现对阴影区域的检测和去除,处理过程中无需提前确定特征判别参数。将所设计的方法在标准高速公路视频数据库中进行测试并应用于实时的视频监控系统,验证结果表明该方法能更加有效的消除阴影,从而准确的检测出运动目标,同时方法对光线变化具有一定的鲁棒性。