Multi-robot Mapping Using Omnidirectional-Vision SLAM Based on Fisheye Images

Choi, Youn-Seok; Kwon, Kee-Koo; Lee, Soo In; Choi, Jeong-Won; Lee, Suk-Gyu

doi:10.4218/etrij.14.0114.0584

Cited by 15 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, some researchers have shown that it is feasible to extend these classical approaches to be used with omnidirectional vision. In this line, Choi et al [114] propose an algorithm to create maps, which is based on an object extraction method, using Lucas-Kanade optical flow motion detection from the images obtained by an omnidirectional vision system. The algorithm uses the outer point of motion vectors as feature points of the environment.…”

Section: Map Building and Subsequent Localizationmentioning

confidence: 99%

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

2017

View full text Add to dashboard Cite

Nowadays, the field of mobile robotics is experiencing a quick evolution, and a variety of autonomous vehicles is available to solve different tasks. The advances in computer vision have led to a substantial increase in the use of cameras as the main sensors in mobile robots. They can be used as the only source of information or in combination with other sensors such as odometry or laser. Among vision systems, omnidirectional sensors stand out due to the richness of the information they provide the robot with, and an increasing number of works about them have been published over the last few years, leading to a wide variety of frameworks. In this review, some of the most important works are analysed. One of the key problems the scientific community is addressing currently is the improvement of the autonomy of mobile robots. To this end, building robust models of the environment and solving the localization and navigation problems are three important abilities that any mobile robot must have. Taking it into account, the review concentrates on these problems; how researchers have addressed them by means of omnidirectional vision; the main frameworks they have proposed; and how they have evolved in recent years.

show abstract

Section: Map Building and Subsequent Localizationmentioning

confidence: 99%

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

2017

View full text Add to dashboard Cite

show abstract

“…Especially, it is efficient that many robots make their own maps for different regions and fuse to update an entire map with sub-maps if space is very large and complex. Choi et al [1] proposed a global mapping algorithm for multiple robots from an omnidirectional-vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas-Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots.…”

Section: Introductionmentioning

confidence: 99%

A method of map updating for global path planning of a robot

Park

Cho

2015

2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

View full text Add to dashboard Cite

A robot should make an adequate global path to offer smooth services to users. For this purpose, accurate environmental information, i.e. map, and continual map updating is needed. In this paper, we suggest a method of map updating for a global path planning of a robot. In this method, we first define the process of creating of map update factors. And then, a step for usage of map update factors is shown. Using this method, a robot can perform its mission with reduction of task load which is needed for collision avoidance in complex environment.

show abstract

“…Scene recognition is crucial to applications such as Autonomous Driving [QLW*18, WJ19, SWL19] and Simultaneous Localization and Mapping (SLAM) [GMH19, CKL*14. Due to the diversity nature of scenes, it is not practical to standardize the category for each scene as is done in object recognition.…”

Section: Introductionmentioning

confidence: 99%

SRNet: A 3D Scene Recognition Network using Static Graph and Dense Semantic Fusion

Fan

Liu

et al. 2020

Computer Graphics Forum

View full text Add to dashboard Cite

Point cloud based 3D scene recognition is fundamental to many real world applications such as Simultaneous Localization and Mapping (SLAM). However, most of existing methods do not take full advantage of the contextual semantic features of scenes. And their recognition abilities are severely affected by dynamic noise such as points of cars and pedestrians in the scene. To tackle these issues, we propose a new Scene Recognition Network, namely SRNet. In this model, to learn local features without being affected by dynamic noise, we propose Static Graph Convolution (SGC) module, which are then stacked as our backbone. Next, to further avoid dynamic noise, we introduce a Spatial Attention Module (SAM) to make the feature descriptor pay more attention to immovable local areas that are more relevant to our task. Finally, in order to make a more profound sense of the scene, we design a Dense Semantic Fusion (DSF) strategy to integrate multi-level features during feature propagation, which helps the model deepen its understanding of the contextual semantics of scenes. By utilizing these designs, SRNet can map scenes to discriminative and generalizable feature vectors, which are then used for finding matching pairs. Experimental studies demonstrate that SRNet achieves new state-of-the-art on scene recognition and shows good generalization ability to other point cloud based tasks.

show abstract

Multi-robot Mapping Using Omnidirectional-Vision SLAM Based on Fisheye Images

Cited by 15 publications

References 30 publications

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

A method of map updating for global path planning of a robot

SRNet: A 3D Scene Recognition Network using Static Graph and Dense Semantic Fusion

Contact Info

Product

Resources

About