LDSO：具有回環檢測的直接稀疏里程計：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.
摘要本文將直接稀疏里程法（DSO）[1]推廣到一種具有閉環檢測和姿態圖優化（LDSO）的單目視覺沖擊系統。作為一種直接的技術，DSO可以利用任何具有足夠強度梯度的圖像像素，這使得它即使在沒有特征的區域也很堅固。LDSO保留了這種魯棒性，同時通過在跟蹤前端支持角特征來確保其中一些點的可重復性。這種重復性允許使用傳統的基於特征的單詞包（bow）方法可靠地檢測循環關閉候選。環閉合候選是驗證幾何和SIM（3）的相對姿態約束共同最小化二維和三維幾何誤差項估計。這些約束與從DSO滑動窗口優化中提取的相對姿態的共可見性圖融合。我們對公開可用數據集的評估表明，改進的點選擇策略保留了跟蹤精度和魯棒性，集成的姿態圖優化顯著減少了累積的旋轉、平移和比例漂移，從而產生了與最先進的特征B相當的總體性能。即使沒有全局包調整，也可以使用ASED系統。
基於立體視覺里程計和語義的室內環境空中機器人定位：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.
摘要本文提出了一種基於立體視覺里程計（VO）和室內環境語義信息的微型航空機器人粒子過濾局部化方法。粒子過濾器的預測階段是使用立體VO算法估計的航空機器人的三維姿態進行的。這個預測的三維姿勢是用慣性和語義測量來更新的。該算法分兩個階段處理語義測量；最后，基於預先訓練的深度學習（DL）的目標檢測器用於實時檢測RGB光譜中的目標。其次，從被測物體對應的三維點雲出發，對其主要水平面進行分割，估計其相對位置，並用新的檢測方法增加先驗圖。然后利用增廣后的地圖，對航空機器人進行無漂移姿態估計。我們在幾個真實的飛行實驗中驗證了我們的方法，在這些實驗中，我們將其與地面實況和最先進的視覺沖擊方法進行比較。
使用無參數統計和聚類實現SLAM中識別物體的定位：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.
簡介——傳統的同步定位與建圖（SLAM）方法基於點、線和平面來建圖。這些地圖在視覺上接近於環境但是沒有任何的關於環境中的物體的語義或者信息。最近的關於機器學習中的進步通過大數量的物體使得物體識別變得高度准確可信。物體識別可以有效地幫助SLAM在建圖過程中將語義包含進來。其中主要障礙之一是隨着時間的推移，檢測到的對象之間的數據關聯。我們展示了一種非參數統計方法來解決連續幀上檢測到的物體之間的數據關聯。然后，我們使用無監督聚類方法來識別地圖中存在的對象。以上整個過程可以與SLAM並行運行。通過對多個公共數據集的分析，證明了該算法的有效性，實現了在SLAM中對目標的定位。