泡泡机器人SLAM 2019

LDSO：具有回环检测的直接稀疏里程计：LDSO:Direct Sparse Odometry with Loop Closure

Abstract—In this paper we present an extension of Direct Sparse Odometry (DSO) [1] to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO). As a direct technique, DSO can utilize any image pixel with sufficient intensity gradient, which makes it robust even in featureless areas. LDSO retains this robustness, while at the same time ensuring repeatability of some of these points by favoring corner features in the tracking frontend. This re- peatability allows to reliably detect loop closure candidates with a conventional feature-based bag-of-words (BoW) approach. Loop closure candidates are verified geometrically and Sim(3) relative pose constraints are estimated by jointly minimizing 2D and 3D geometric error terms. These constraints are fused with a co-visibility graph of relative poses extracted from DSO’s sliding window optimization. Our evaluation on publicly available datasets demonstrates that the modified point selection strategy retains the tracking accuracy and robustness, and the integrated pose-graph optimization significantly reduces the accumulated rotation-, translation- and scale-drift, resulting in an overall performance comparable to state-of-the-art feature- based systems, even without global bundle adjustment.

摘要本文将直接稀疏里程法（DSO）[1]推广到一种具有闭环检测和姿态图优化（LDSO）的单目视觉冲击系统。作为一种直接的技术，DSO可以利用任何具有足够强度梯度的图像像素，这使得它即使在没有特征的区域也很坚固。LDSO保留了这种鲁棒性，同时通过在跟踪前端支持角特征来确保其中一些点的可重复性。这种重复性允许使用传统的基于特征的单词包（bow）方法可靠地检测循环关闭候选。环闭合候选是验证几何和SIM（3）的相对姿态约束共同最小化二维和三维几何误差项估计。这些约束与从DSO滑动窗口优化中提取的相对姿态的共可见性图融合。我们对公开可用数据集的评估表明，改进的点选择策略保留了跟踪精度和鲁棒性，集成的姿态图优化显著减少了累积的旋转、平移和比例漂移，从而产生了与最先进的特征B相当的总体性能。即使没有全局包调整，也可以使用ASED系统。

基于立体视觉里程计和语义的室内环境空中机器人定位：Stereo Visual Odometry and Semantics based Localization of Aerial_Robots in Indoor Environments

Abstract—In this paper we propose a particle filter local- ization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of the particle filter is performed using the 3D pose of the aerial robot estimated by the stereo VO algorithm. This predicted 3D pose is updated using inertial as well as semantic measurements. The algorithm processes semantic measurements in two phases; firstly, a pre-trained deep learning (DL) based object detector is used for real time object detections in the RGB spectrum. Secondly, from the corresponding 3D point clouds of the detected objects, we segment their dominant horizontal plane and estimate their relative position, also augmenting a prior map with new detections. The augmented map is then used in order to obtain a drift free pose estimate of the aerial robot. We validate our approach in several real flight experiments where we compare it against ground truth and a state of the art visual SLAM approach.

摘要本文提出了一种基于立体视觉里程计（VO）和室内环境语义信息的微型航空机器人粒子过滤局部化方法。粒子过滤器的预测阶段是使用立体VO算法估计的航空机器人的三维姿态进行的。这个预测的三维姿势是用惯性和语义测量来更新的。该算法分两个阶段处理语义测量；最后，基于预先训练的深度学习（DL）的目标检测器用于实时检测RGB光谱中的目标。其次，从被测物体对应的三维点云出发，对其主要水平面进行分割，估计其相对位置，并用新的检测方法增加先验图。然后利用增广后的地图，对航空机器人进行无漂移姿态估计。我们在几个真实的飞行实验中验证了我们的方法，在这些实验中，我们将其与地面实况和最先进的视觉冲击方法进行比较。

使用无参数统计和聚类实现SLAM中识别物体的定位：Localization of Classified Objects in SLAM using Nonparametric Statistics and Clustering

Abstract—Traditional Simultaneous Localization and Mapping (SLAM) approaches build maps based on points, lines or planes. These maps visually resemble the environment but without any semantic or information about the objects in the environment. Recent advancements in machine learning have made object detection highly accurate and reliable with large set of objects. Object detection can effectively help SLAM to incorporate semantics in the mapping process. One of the main obstacles is data association between detected objects over time. We demonstrate a nonparametric statistical approach to solve the data association between detected objects over consecutive frames. Then we use an unsupervised clustering method to identify the existence of objects in the map. The complete process can be run in parallel with SLAM. The performance of our algorithm is demonstrated on several public datasets, which shows promising results in locating objects in SLAM.

简介——传统的同步定位与建图（SLAM）方法基于点、线和平面来建图。这些地图在视觉上接近于环境但是没有任何的关于环境中的物体的语义或者信息。最近的关于机器学习中的进步通过大数量的物体使得物体识别变得高度准确可信。物体识别可以有效地帮助SLAM在建图过程中将语义包含进来。其中主要障碍之一是随着时间的推移，检测到的对象之间的数据关联。我们展示了一种非参数统计方法来解决连续帧上检测到的物体之间的数据关联。然后，我们使用无监督聚类方法来识别地图中存在的对象。以上整个过程可以与SLAM并行运行。通过对多个公共数据集的分析，证明了该算法的有效性，实现了在SLAM中对目标的定位。