ORB-SLAM-based tracing and 3D reconstruction for robot using Kinect 2.0

Lu, Qiang; Lin, Hongbo; Wang, Guosheng; Wei, Heng; Wang, Yang

doi:10.1109/ccdc.2017.7979079

Cited by 23 publications

(14 citation statements)

References 12 publications

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“…Furthermore, based on In contrast, the suggested direct method maps and follows picture intensities directly [47]. the characteristics of the ORB algorithm, ORB-SLAM can fix faults [30]. Additionally, it performs well while creating maps, but ORB-SLAM struggles to locate features in dynamic settings.…”

Section: Recent Workmentioning

confidence: 99%

Improving Visual SLAM by Combining SVO and ORB-SLAM2 with a Complimentary Filter to Enhance Indoor Mini-Drone Localization Under Varying Conditions

Basiri¹,

Mariani²,

Neyestani³

et al. 2022

Preprint

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Mini-drones can be used for a variety of tasks, such as weather monitoring, package delivery, search and rescue, and recreation. Their uses are mostly restricted to outside locations with access to the Global Positioning System (GPS) and/or similar systems since their usefulness, safety, and performance substantially rely on ubiquitously accurate positioning and navigation. Indoor localization is getting better, thanks to technologies like Visual Simultaneous Localization and Mapping (V-SLAM). However, more advancements are still required for mini-drone navigation applications with greater safety standards. In this research, a novel method for enhancing indoor mini-drone localization performance is proposed. By merging Oriented Rotated Brief SLAM (ORB-SLAM2), Semi-Direct Monocular Visual Odometry (SVO), and an Adaptive Complementary Filter, the suggested strategy improves V-SLAM approaches (ACF). The findings demonstrate that, when compared to other widely-used indoor localization algorithms, the suggested methodology performs better at estimating location under various situations (low light, low texture, and dynamic environments).

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Section: Recent Workmentioning

confidence: 99%

Improving Visual SLAM by Combining SVO and ORB-SLAM2 with a Complimentary Filter to Enhance Indoor Mini-Drone Localization Under Varying Conditions

Basiri¹,

Mariani²,

Neyestani³

et al. 2022

Preprint

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show abstract

“…However, both studies ignored the importance of the 3D map, and they also cannot reconstruct a complete environmental structure, which made them lose the most important advantage of visual SLAM. Considering how the sparse map cannot be applied to obstacle avoidance and navigation, Qiang et al [17] proposed a dense 3D reconstruction method based on the ORB-SLAM algorithm. The method enables the ORB-SLAM system to construct an octomap [18] based on the octrees and probabilistic occupancy estimation, and the improved ORB-SLAM can complete the map reconstruction in real time using a Kinect 2.0 camera in the real world.…”

Section: Related Workmentioning

confidence: 99%

Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm

et al. 2019

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In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment.

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“…Xu et al (2019) develop a 3D reconstruction system using three RGB-D cameras to capture 3D information, and their method used SIFT features. Lv et al (2017) used a handheld RGB-D camera to construct a point cloud of a desk and a room scene. In their method, the ORB features were used.…”

Section: Introductionmentioning

confidence: 99%

“…Lv et al. (2017) used a handheld RGB‐D camera to construct a point cloud of a desk and a room scene. In their method, the ORB features were used.…”

Section: Introductionmentioning

confidence: 99%

Automatic feature type selection in digital photogrammetry of piping

Tian

Ding

Lin

et al. 2022

Computer aided Civil Eng

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A building information model for pipes already in place is essential in maintenance, for example, mending, reconstruction, and modernizing. However, current point cloud construction methods are not suited to complex piping systems, and many point cloud merging methods perform poorly in complex piping environments. To provide critical functions for constructing pipe point clouds from digital photogrammetry, a feature type selection network (FTSNet) is proposed. First, a digital photogrammetry method combining FTSNet with handcrafted features is proposed. Second, a piping data set of various piping scenes is constructed and evaluated using the developed data capturing device. A training data set and the corresponding network output categories are determined according to pose‐estimation performance using different image feature types. Finally, experiments conducted on the final test data set indicate that, together, digital photogrammetry and FTSNet can improve accuracy, flexibility, and processing speed.

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ORB-SLAM-based tracing and 3D reconstruction for robot using Kinect 2.0

Cited by 23 publications

References 12 publications

Improving Visual SLAM by Combining SVO and ORB-SLAM2 with a Complimentary Filter to Enhance Indoor Mini-Drone Localization Under Varying Conditions

Improving Visual SLAM by Combining SVO and ORB-SLAM2 with a Complimentary Filter to Enhance Indoor Mini-Drone Localization Under Varying Conditions

Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm

Automatic feature type selection in digital photogrammetry of piping

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