测程法系统误差的测量与校核

在UMBmark校核算法的基础上定义了测程法系统误差模型 ,提出了详细的算法来计算差分移动机器人的系统参数及对应的纠正系数 .试验结果表明 :本文提出的方法比UMBmark校核方法更能提高测程法的定位精度 .     

一种结合光流追踪的双目视觉里程计算法

针对特征点法视觉里程计频繁计算和匹配描述子导致系统实时性能变差的问题,提出一种结合光流追踪的双目视觉里程计算法;首先进行初始化,生成初始的关键帧和地图点,随后在追踪线程中使用光流追踪特征点获取匹配关系,计算并优化相机位姿;满足生成关键帧的条件后,将当前帧设置为关键帧,提取图像的ORB特征点,并使用描述子匹配获取与上一关键帧特征点的匹配关系,三角化生成新的地图点;最后在优化线程中将新的关键帧和地图点使用滑动窗口算法进行优化,剔除冗余关键帧和地图点;在KITTI数据集上的实验结果表明,所提出的算法轨迹误差与双目模式下的ORB-SLAM3算法处于同一水平,同时实时性能有大幅度提高,追踪每一帧图像的平均时间从52ms降至16ms,在保证高精度的情况下运行速度大大提高,具有较高的实用价值.     

An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid

There are about 253 million people with visual impairment worldwide. Many of them use a white cane and/or a guide dog as the mobility tool for daily travel. Despite decades of efforts, electronic navigation aid that can replace white cane is still research in progress. In this paper, we propose an RGB-D camera based visual positioning system (VPS) for real-time localization of a robotic navigation aid (RNA) in an architectural floor plan for assistive navigation. The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry (DVIO) method and a particle filter localization (PFL) method. DVIO estimates RNA’s pose by using the data from an RGB-D camera and an inertial measurement unit (IMU). It extracts the floor plane from the camera’s depth data and tightly couples the floor plane, the visual features (with and without depth data), and the IMU’s inertial data in a graph optimization framework to estimate the device’s 6-DOF pose. Due to the use of the floor plane and depth data from the RGB-D camera, DVIO has a better pose estimation accuracy than the conventional VIO method. To reduce the accumulated pose error of DVIO for navigation in a large indoor space, we developed the PFL method to locate RNA in the floor plan. PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose. Based on VPS, an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space. Experimental results demonstrate that: 1) DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation (18 Hz pose update rate) on a UP Board computer; 2) PFL reduces the DVIO-accrued pose error by 82.5% on average and allows for accurate wayfinding (endpoint position error ≤ 45 cm) in large indoor spaces.      

Head Movements for Depth Perception: Praying Mantis versus Pigeon

Inspired by the abilities of both the praying mantis and the pigeon to judge distance by use of motion-based visually mediated odometry, we create miniature models for depth estimation that are similar to the head movements of these animals. We develop mathematical models of the praying mantis and pigeon visual behavior and describe our implementations and experimental environment. We investigate structure from motion problems when images are taken from a camera whose focal point is translating according to each of the biological models. This motion in the first case is reminiscent of a praying mantis peering its head left and right, apparently to obtain depth perception, hence the moniker mantis head camera. In the second case this motion is reminiscent of a pigeon bobbing its head back and forth, also apparently to obtain depth perception, hence the moniker pigeon head camera. We present the performance of the mantis head camera and pigeon head camera models and provide experimental results and error analysis of the algorithms. We provide the comparison of the definitiveness of the results obtained by both models. The precision of our mathematical model and its implementation is consistent with the experimental facts obtained from various biological experiments.     

基于卷积神经网络与扩展卡尔曼滤波的 单目视觉惯性里程计

针对单目相机采集室外图像易受环境光照影响、尺度存在不确定性的缺点,以及利用神经网络进行位姿估计不准确的问题,提出一种基于卷积神经网络(CNN)与扩展卡尔曼滤波(EKF)的单目视觉惯性里程计。采用神经网络取代传统里程计中基于几何约束的视觉前端,将单目相机输出的估计值作为测量更新,并通过神经网络优化EKF的误差协方差。利用EKF融合CNN输出的单目相机位姿和惯性测量单元(IMU)数据,优化CNN的位姿估计,补偿相机尺度信息与IMU累计误差,实现无人系统运动位姿的更新和估计。相比于使用单目图像的深度学习算法Depth-VO-Feat,所提算法融合单目图像和IMU数据进行位姿估计,KITTI数据集中09序列的平动、转动误差分别减少45.4%、47.8%,10序列的平动、转动误差分别减少68.1%、43.4%。实验结果表明所提算法能进行更准确的位姿估计,验证了算法的准确性和可行性。     

基于深度学习的视觉同时定位与建图研究进展

随着机器视觉的不断发展,视觉传感器其小巧轻便、价格低廉等优势,使得视觉同时定位与建图(VSLAM)越来越受人们关注,深度学习为处理VSLAM问题提供了新的方法与思路。本文综述了近年来基于深度学习的VSLAM方法。首先回顾了VSLAM的发展历程,系统阐释了VSLAM的基本原理与组成结构。然后从视觉里程计(VO)、回环检测与建图3个方面分析各类基于深度学习的方法,从特征提取与特征匹配、深度估计与位姿估计及关键帧选择等3个部分阐述了深度学习在VO中的应用;基于场景表达方式的不同,总结了几何建图、语义建图及广义建图中的深度学习方法。接着介绍了目前VSLAM常用的各种数据集以及性能评估指标。最后指出了目前VSLAM面临的难题与挑战,展望未来深度学习与VSLAM结合的研究趋势与发展方向。     

基于平面单应性的单目视觉里程计设计

针对如何准确获取位姿信息来实现移动机器人的避障问题,提出一种可用于实时获取移动机器人位姿的单目视觉里程计算法。该算法利用单目摄像机获取连续帧间图像路面SURF（Speeded Up Robust Features）特征点;并结合极线几何约束来解决路面特征点匹配较难的问题,通过计算平面单应性矩阵获取移动机器人的位姿变化。实验结果表明该算法具有较高的精度和实时性。     

一种基于VINS的视觉里程计改进方法

同时定位与地图构建(SLAM)是当今机器人领域的主要研究课题之一。针对如何根据图像估计相机位姿问题,提出一种基于VINS的视觉里程计改进方法(ORLK-VINS)。首先,通过双目相机获取图像信息;其次,将图像信息进行直方图均衡化处理,使图像对比度和亮度得到改善;然后,对原图像特征提取算法进行改进,引入ORB算法中带有方向的FAST角点;最后再将提取的特征点进行正反向的LK光流跟踪匹配,保证匹配特征点的精确性。实验表明,经过改进后的视觉里程计相较于主流的VINS-Fusion算法,在某些场景下拥有更好的实时性和定位准确性。     

基于深度相机的SLAM算法评测

针对3种典型的基于深度相机的同步定位与地图构建(SLAM)算法,包括RGB-D SLAM V2,RTAB-Map和DVO SLAM,介绍这3种SLAM算法的理论特点。采用两个公开的SLAM数据集,包括TUM数据集和ICL-NUIM数据集,进行SLAM算法的评测,评测指标包括SLAM算法的精确度、运行性能以及鲁棒性。评测的实验结果表明,在选择基于深度相机的SLAM算法时:如果考虑精确度和鲁棒性优先于运行性能,则选择RGB-D SLAM V2;如果考虑运行性能和鲁棒性优先于精确度,则选择DVO SLAM;如果考虑精确度和运行性能优先于鲁棒性,则选择RTAB-Map。     

基于快速 ACE 算法的视觉里程计图像增强方法

为提高同时定位与地图构建(simultaneous localization and mapping,SLAM)在室内定位的不同场景下的鲁棒性,应对室内场景纹理少,光线差等极端环境的挑战.通过利用改进快速自动彩色增强(automatic color enhancement,ACE)图像增强技术,优化定向FAST和旋转B...