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In this paper, we present an approach for producing side scan sonar image mosaicking under a robust SLAM scheme. A Pose Graph based SLAM algorithm is used to perform a correction over the sensor trajectory for enabling image registration, using observation constraints extracted from the images. However, due to the operational context, the available odometry data carries a high degree of uncertainty...
This paper proposes a geometric feature-based method to solve the Simultaneous Localization and Mapping (SLAM) problem in an unknown structured environment using a short range and low Field of View (FoV) measurement unit such as Kinect sensor. A RANdom SAmple Consensus (RANSAC) based algorithm is used for feature detection, and a grid-based point cloud segmentation method has been introduced to improve...
A method that optimizes visual odometry, especially using visual odometry in the scene with absence of features is proposed in this paper. First, in order to estimate the pose of camera when the effect of using feature-based method is not good enough, direct method is implemented as the solution. Second, comparing with traditional visual odometry method, this method takes the environment restriction...
In this paper, we propose a monocular SLAM system for robotic urban search and rescue (USAR), based on which most USAR tasks (e.g. localization, mapping, exploration and object recognition) can be fulfilled by rescue robots with only a single camera. The proposed system can be a promising basis to implement fully autonomous rescue robots. However, the feature-based map built by the monocular SLAM...
Feature extraction is a key component of a Monocular Simultaneous Localization and Mapping (Monocular SLAM) system which permits to extract features and can also reliably track them over frames. In this paper, a novel approach for Monocular SLAM is proposed. This approach uses the information on the camera displacement and image saliency to adequately extract stable and suitable features, ones that...
In this paper, an effective approach to Simultaneous Localization and Mapping (SLAM) based on RGB-D images is presented toward autonomous operation of a Leg/Arm Composite Mobile Robot (LACMR), in which depth information and its effective features are utilized sufficiently so as to overcome some malpractice in conventional methods and enhance the performance of SLAM. Our scheme can be narrated as follows...
This paper proposes a method that realizes moving object detection (MOD) and static obstacle detection (SOD) in real time utilizing the fisheye cameras of the around viewing system (AVM). The topview of the AVM is used to calculate the vehicles movement between two frames using homograph estimation. Image features are detected and tracked evenly using cell detection technique. Then the features are...
Monocular visual odometry algorithm has been widely used to estimate the pose of aerial robots in GPS denied environments. However, the pure visual system usually has poor robustness in large scale environments. This paper presents a pose estimation algorithm which fuses monocular visual and inertial data using the monocular ORB-SLAM algorithm as the visual framework. Firstly, the scale estimation...
Loop closure detection is an important part of visual simultaneous location and mapping (SLAM) system. Most of traditional loop closure detection approaches using hand-crafted features often lack robustness with respect to object occlusions and illumination changes, especially for the complicated indoor environment. Recently, convolutional neural network (CNN) makes a huge impact on many computer...
Loop closure detection is important in simultaneous localization and mapping (SLAM) systems. In this paper, Generative Adversarial Networks (GAN), an unsupervised deep architecture is employed to detect the loop closure for vision-based SLAM systems. Instead of extracting handcrafted features like SIFT, SURF or ORB. Generative Adversarial Networks are based on image features. Similar to the task about...
A fast method for mobile robot 3D SLAM (simultaneous localization and mapping) is presented to address the problem of 3D modeling in complex indoor environment. According to the camera calibration model and the image feature extraction and matching procedure, the association between two 3D point clouds can be established. On the basis of the RANSAC (random sample consensus) algorithm, the correspondence...
Robotic simultaneous localization and mapping (SLAM) confronts extreme challenge in collapsed, cluttered, GPS-signal unreliable environments of search and rescue (SAR). Our improved SLAM methods aim to mobile robot performing SAR requirements which comprise the significant objects identification, loop closure perceiving, exploration area coverage, and the other performances. We developed efficient...
This paper presents a novel strategy addressing visual SLAM with enhancement of data association method. Hyper graph theory and transformation was incorporated within cooperative visual SLAM. The research presented a synthetic approach to fulfill a cooperative data association and fusion strategy for multiple UAVs equipped with stereo vision cameras encountered with indistinct imaging, where conventional...
This paper is concerned of the loop closure detection problem, which is one of the most critical parts for visual Simultaneous Localization and Mapping (SLAM) systems. Most of state-of-the-art methods use hand-crafted features and bag-of-visual-words (BoVW) to tackle this problem. Recent development in deep learning indicates that CNN features significantly outperform hand-crafted features for image...
Visual Simultaneous Localization and Mapping (VSLAM) requires feature detection on visual data. In indoor scenes that include architectures such as plain walls and doors, there are no or less corners detected, in such cases tracking will be lost. To overcome this situation we track different types of features that help in feature extraction even in textureless scenes. Line features are used to get...
ORB-SLAM is a feature-based simultaneous localization and mapping (SLAM) system. It has achieved good results in tracking, mapping and loop closing. However, the map created by ORB-SLAM with the monocular camera can not get the real scale. This paper presents an improving ORB SLAM system that helps to alleviate this issue by defining a baseline initialization procedure. We take two relative poses...
This paper proposed a Mixed Match SLAM embedded with two match methods, planar patch based and visual descriptor based. Planar patch based method eliminates the cost of feature extraction for motion estimation. Visual descriptor based method is more robust for wide baseline match and loop closing. We use a reasonable statistic criterion to determine keyframe for redundancy avoidance. An efficient...
The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots current local sub-maps in cases where every robot is considered as a mobile distributed particle, and each robot...
Loop Closure Detection (LCD) has been proved to be extremely useful in global consistent visual Simultaneously Localization and Mapping (SLAM) and appearance-based robot relocalization. Methods exploiting binary features in bag of words representation have recently gained a lot of popularity for their efficiency, but suffer from low recall due to the inherent drawback that high dimensional binary...
In visual simultaneous localization and mapping (SLAM) field, especially for feature based stereo-SLAM, data association is one of the most important and time-consuming sub-tasks. In this paper, we investigate the roles of different measured features during the data association process and present a new hybrid feature parametrization approach for stereo SLAM, which only selects a subset of the matched...
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