Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

In the process of sulfur recovery, H2S and SO2 concentrations reflect the effectiveness and reliability of the recovery process. However, the concentration of H2S and SO2 in the process of sulfur recovery is difficult to be measured by online analysis instrument, so the soft sensing modeling method is often used to analyze the system. Because SRU system has strong nonlinear characteristics and dynamic process characteristics,traditional soft sensing modeling methods are often limited in use. Long Short-Term Memory (LSTM)Artificial neural networks show strong ability in processing nonlinear data and dynamic data. Therefore, LSTM soft sensing method is used in this paper to systematically analyze the sulfur recovery process.

The current visual simultaneous localization and mapping (SLAM) systems require the use of matched feature point pairs to estimate camera pose and construct environmental maps. Therefore, they suffer from poor performance of the visual feature matchers. To address this problem, a visual SLAM using deep feature matcher is proposed, which is mainly composed of three parallel threads: Visual Odometry, Backend Optimizer and LoopClosing. In the Visual Odometry, the deep feature extractor with convolutional neural networks is utilized for extracting feature points in each image frame. Then, the deep feature matcher is used for obtaining the corresponding feature landmark pairs. Afterwards, a fusion method based on the last and the reference frame is proposed for camera pose estimation. The Backend Optimizer is designed to execute local bundle adjustment for a part of camera poses and landmarks (map points). While LoopClosing, consisting of a lightweight deep loop closure detector and the same matcher as the one used in Visual Odometry is utilized for loop correction based on pose graph. The proposed system has been tested extensively on most of benchmark KITTI odometry dataset. The experimental results show that our system yields better performance than the existing visual SLAM systems. It can not only run in real-time at a speed of 0.08 s per frame, but also reduce estimation error by at least 0.1 m.

In this paper we consider a Simultaneous Localization and Mapping (SLAM) problem for a moving agent using Visual Odometry (VO) while measuring the range from a set of Ultra Wide Band (UWB) antennas, deployed in unknown position in the environment. The solution approach is based on a switching observer which, under standard working conditions, for each observed UWB, uses a two dimensional Extended Kalman Filter (EKF) providing an estimate of the range and bearing of the observed UWB with respect to the agent. This information is then used in a Robust EKF algorithm which solves the SLAM problem with performances that, even before closing the loop, are comparable to the ones that a VO algorithm (namely ORB-SLAM2) would obtain only after closing the loop. Moreover, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed UWB. When the Visual Odometry is not available, the switching observer uses an auxiliary EKF to provide an estimate of the agent position. This makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects, and automatically adapts to sensor failures with resilience (e.g. when Visual Odometry or UWB measurements are not available).

Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.

<abstract><p>For the autonomous and intelligent operation of robots in unknown environments, simultaneous localization and mapping (SLAM) is essential. Since the proposal of visual odometry, the use of visual odometry in the mapping process has greatly advanced the development of pure visual SLAM techniques. However, the main challenges in current monocular odometry algorithms are the poor generalization of traditional methods and the low interpretability of deep learning-based methods. This paper presented a hybrid self-supervised visual monocular odometry framework that combined geometric principles and multi-frame temporal information. Moreover, a post-odometry optimization module was proposed. By using image synthesis techniques to insert synthetic views between the two frames undergoing pose estimation, more accurate inter-frame pose estimation was achieved. Compared to other public monocular algorithms, the proposed approach showed reduced average errors in various scene sequences, with a translation error of $ 2.211\% $ and a rotation error of $ 0.418\; ^{\circ}/100m $. With the help of the proposed optimizer, the precision of the odometry algorithm was further improved, with a relative decrease of approximately 10$ \% $ intranslation error and 15$ \% $ in rotation error.</p></abstract>

To solve the accurate positioning problem of mobile robots, simultaneous localization and mapping (SLAM) or visual odometry (VO) based on visual information are widely used. However, most visual SLAM or VO cannot meet the accuracy requirements in dynamic indoor environments. This paper proposes a robust visual odometry based on deep learning to eliminate feature points matching error. However, when a camera and dynamic objects are in relative motion, the frames of camera will produce ghosting, especially in high-dynamic environments, which bring additional positioning error; in view of this problem, a novel method based on the average optical flow value of the dynamic region is proposed to identify feature points of the ghosting, and then the feature points of the ghosting and dynamic region are removed. After the remaining feature points are matched, we use a non-linear optimization method to calculate the pose. The proposed algorithm is tested on TUM RGB-D dataset, and the results show that our VO improves the positioning accuracy than other robust SLAM or VO and is strongly robust especially in high-dynamic environments.

Visual odometry is a core component of many visual navigation systems like visual simultaneous localization and mapping (SLAM). Grid cells have been found as part of the path integration system in the rat's entorhinal cortex, and they provide inputs for place cells in the rat's hippocampus. Together with other cells, they constitute a positioning system in the brain. Some computational models of grid cells based on continuous attractor networks have also been proposed in the computational biology community, and using these models, self-motion information can be integrated to realize dead-reckoning. However, so far few researchers have tried to use these computational models of grid cells directly in robot visual navigation in the robotics community. In this paper, we propose to apply continuous attractor network model of grid cells to integrate the robot's motion information estimated from the vision system, so a biologically inspired visual odometry can be realized. The experimental results show that good dead-reckoning can be achieved for different mobile robots with very different motion velocities using our algorithm. We also implement a full visual SLAM system by simply combining the proposed visual odometry with a quite direct loop closure detection derived from the well-known RatSLAM, and comparable results can be achieved in comparison with RatSLAM.

Abstract. In this article we present a new method for visual odometry based on a focused plenoptic camera. This method fuses the depth data gained by a monocular Simultaneous Localization and Mapping (SLAM) algorithm and the one received from a focused plenoptic camera. Our algorithm uses the depth data and the totally focused images supplied by the plenoptic camera to run a real-time semi-dense direct SLAM algorithm. Based on this combined approach, the scale ambiguity of a monocular SLAM system can be overcome. Furthermore, the additional light-field information highly improves the tracking capabilities of the algorithm. Thus, visual odometry even for narrow field of view (FOV) cameras is possible. We show that not only tracking profits from the additional light-field information. By accumulating the depth information over multiple tracked images, also the depth accuracy of the focused plenoptic camera can be highly improved. This novel approach improves the depth error by one order of magnitude compared to the one received from a single light-field image.

[abstFig src='/00290002/10.jpg' width='300' text='Visual odometry + trajectory correction' ] This paper proposes the ORB-SHOT SLAM or OS-SLAM, which is a novel method of 3D loop closing for trajectory correction of RGB-D visual SLAM. We obtain point clouds from RGB-D sensors such as Kinect or Xtion, and we use 3D SHOT descriptors to describe the ORB corners. Then, we train an offline 3D vocabulary that contains more than 600,000 words by using two million 3D descriptors based on a large number of images from a public dataset provided by TUM. We convert new images to bag-of-visual-words (BoVW) vectors and push these vectors into an incremental database. We query the database for new images to detect the corresponding 3D loop candidates, and compute similarity scores between the new image and each corresponding 3D loop candidate. After detecting 2D loop closures using ORB-SLAM2 system, we accept those loop closures that are also included in the 3D loop candidates, and we assign them corresponding weights according to the scores stored previously. In the final graph-based optimization, we create edges with different weights for loop closures and correct the trajectory by solving a nonlinear least-squares optimization problem. We compare our results with several state-of-the-art systems such as ORB-SLAM2 and RGB-D SLAM by using the TUM public RGB-D dataset. We find that accurate loop closures and suitable weights reduce the error on trajectory estimation more effectively than other systems. The performance of ORB-SHOT SLAM is demonstrated by 3D reconstruction application.

Visual odometry (VO) plays a vital role in Simultaneous Localization and Mapping (SLAM). Most of the current learning-based VO methods utilize Convolutional Neural Network (CNN) as framework. However, CNN is weak in integrating global context information. In the design of loss function, the majority of these methods ignore the restraint relationship between translation and rotation prediction. In this work we propose an end-to-end global-context-aware visual odometry scheme with Epipolar-Geometry-constrained loss function to estimate the relative 6-DoF poses of monocular camera. The proposed scheme designs an augmented-attention-enhanced global context block on top of contextual CNN to learn the long-range dependencies and internal correlation. To overcome the problem of mutual restraint between the translation and rotation errors, an Epipolar-Geometry-constrained loss function is developed to simultaneously improve the prediction accuracy of both translation and rotation. The evaluation results on public datasets and self-collected dataset show that the proposed system outperforms state-of-the-art learning-based VO methods with a large margin.

This paper describes in a detailed manner a method to implement a simultaneous localization and mapping (SLAM) system based on monocular vision for applications of visual odometry, appearance‐based sensing, and emulation of range‐bearing measurements. SLAM techniques are required to operate mobile robots ina prioriunknown environments using only on‐board sensors to simultaneously build a map of their surroundings; this map will be needed for the robot to track its position. In this context, the 6‐DOF (degree of freedom) monocular camera case (monocular SLAM) possibly represents the harder variant of SLAM. In monocular SLAM, a single camera, which is freely moving through its environment, represents the sole sensory input to the system. The method proposed in this paper is based on a technique called delayed inverse‐depth feature initialization, which is intended to initialize new visual features on the system. In this work, detailed formulation, extended discussions, and experiments with real data are presented in order to validate and to show the performance of the proposal.

Localization and mapping in a dynamic scene is a crucial problem for the indoor visual simultaneous localization and mapping (SLAM) system. Most existed visual odometry (VO) or SLAM systems are based on the assumption that the environment is static. The performance of a SLAM system may degenerate when it is operated in a severely dynamic environment. The assumption limits the applications of RGB-D SLAM in the dynamic environment. In this paper, we propose a workflow to segment the objects accurately, which will be marked as the potentially dynamic-object area based on the semantic information. A novel approach for motion detection and removal from the moving camera is introduced. We integrate the semantics-based motion detection and the segmentation approach with an RGB-D SLAM system. To evaluate the effectiveness of the proposed approach, we conduct the experiments on the challenging dynamic sequences of TUM-RGBD datasets. The experimental results suggest that our approach improves the accuracy of localization and outperforms the state-of-the-art dynamic-removal-based SLAM system in both severely dynamic and slightly dynamic scenes.

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.

In this paper we describe VOPT (Visual Odometry by Patch Tracking), a robust algorithm for visual odometry, which is able to operate with sparse or dense maps computed by simultaneous localization and mapping (SLAM) algorithms. By using an iterative multi-scale procedure, VOPT is able to estimate the individual motion, photometric correction and reliability tracking confidence of a set of planar patches. In order to overcome the high computational cost of the patch adjustment, we use a GPU-based least-square solver, achieving real-time performance. The algorithm can also be used as a building block to other procedures for automatic initialization and recovery of 3D scene. Our tests show that VOPT outperforms the well-known PTAMM and the state-of-art ORB-SLAM algorithm in challenging videos using the same input maps.

A multi-sensor fusion simultaneous localization and mapping(SLAM) method based on factor graph optimization that can adaptively modify the weight of the graph factor is proposed in this study, to enhance the localization and mapping capability of autonomous robots in tough situations. Firstly, the algorithm fuses multi-lines lidar, monocular camera, and inertial measurement unit(IMU) in the odometry. Second, the factor graph is constructed using lidar and visual odometry as the unary edge and binary edge constraints, respectively, with the motion determined by IMU odometry serving as the primary odometry in the system. Finally, different increments of IMU odometry, lidar odometry and visual odometry are computed as favor factors to realize the adaptive adjustment of the factor’s weight. The suggested method has greater location accuracy and a better mapping effect in complex situations when compared to previous algorithms.