Semi-Direct Visual Odometry and Mapping System with RGB-D Camera

In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camera motion of two consecutive RGB-D frames by minimizing the photometric error.To permit outliers and noise,a robust sensor model built upon the t-distribution and an error function mixing depth and photometric errors are used to enhance the accuracy and robustness.Local graph optimization based on key frames is used to reduce the accumulative error and refine the local map.The loop closure detection method,which combines the appearance similarity method and spatial location constraints method,increases the speed of detection.Experimental results demonstrate that the proposed approach achieves higher accuracy on the motion estimation and environment reconstruction compared to the other state-of-the-art methods. Moreover,the proposed approach works in real-time on a laptop without a GPU,which makes it attractive for robots equipped with limited computational resources.

基于动态语义特征的视觉SLAM系统

针对视觉SLAM(Simultaneous Localization and Mapping)在真实场景下出现动态物体(如行人,车辆、动物)等影响算法定位和建图精确性的问题,基于ORB-SLAM3(Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3)提出了YOLOv3-ORB-SLAM3算法。该算法在ORB-SLAM3的基础上增加了语义线程,采用动态和静态场景特征提取双线程机制:语义线程使用YOLOv3对场景中动态物体进行语义识别目标检测,同时对提取的动态区域特征点进行离群点剔除;跟踪线程通过ORB特征提取场景区域特征,结合语义信息获得静态场景特征送入后端,从而消除动态场景对系统的干扰,提升视觉SLAM算法定位精度。利用TUM(Technical University of Munich)数据集验证,结果表明YOLOv3-ORB-SLAM3算法在单目模式下动态序列相比ORB-SLAM3算法ATE(Average Treatment Effect)指标下降30%左右,RGB-D(Red,Green and Blue-Depth)模式下动态序列ATE指标下降10%,静态序列未有明显下降。

实时三维重建算法的实现——基于Kinect与单目视觉SLAM的三维重建

作为计算机视觉技术的一个重要分支,基于单目视觉的三维重建技术以其要求简单、成本低廉、易于实现等优点,得到了越来越多的关注。在室内环境下就智能机器人的同步定位以及环境地图创建(SLAM)算法展开了研究,引入RGB-D相机Kinect直接获取3D场景的深度信息,实现了一种基于单目视觉SLAM与Kinect的实时三维重建方法。

机器人定位与导航研究平台的改进

机器人的即时定位与地图创建是近年来移动机器人的研究热点。移动机器人通过传感器感知环境和自身状态,进而实现自主运动。具体来说就是机器人在一个未知的环境中,获取信息、分析环境,确定自己在该环境中的具体位置。作为实验对象的移动机器人,需要有很强的负载能力,并且能够长时间运行。该文针对原有机器人研究平台LABO-3的不足,使用新的EMC-700移动机器人进行替代,并对其进行了改进。为以后的移动机器人即时定位与地图创建的实验创造了良好的硬件基础。

面向高动态环境的ORB-SLAM3算法优化

ORB-SLAM3(simultaneous localization and mapping)是当前最优秀的视觉SLAM算法之一,然而其基于静态环境的假设导致算法在高动态环境下精度不佳甚至定位失败。针对这一问题,提出一种结合光流和实例分割的动态特征点去除方法,以提高ORB-SLAM3在高动态环境下的定位精度。并且在TUM数据集上进行了RGB-D相机模式和单目相机模式的实验,实验结果表明了该方法的有效性。

Elaborate Scene Reconstruction with a Consumer Depth Camera

A robust approach to elaborately reconstruct the indoor scene with a consumer depth camera is proposed in this paper. In order to ensure the accuracy and completeness of 3D scene model reconstructed from a freely moving camera, this paper proposes new 3D reconstruction methods, as follows： 1） Depth images are processed with a depth adaptive bilateral filter to effectively improve the image quality; 2） A local-to-global registration with the content-based segmentation is performed, which is more reliable and robust to reduce the visual odometry drifts and registration errors; 3） An adaptive weighted volumetric method is used to fuse the registered data into a global model with sufficient geometrical details. Experimental results demonstrate that our approach increases the robustness and accuracy of the geometric models which were reconstructed from a consumer-grade depth camera.