Maps for Mobile Robots

2013 ◽  
pp. 254-297
Keyword(s):  
Mobile Robot ◽  
Mathematical Models ◽  
Mobile Robots ◽  
Sensory Data ◽  
The Third ◽  
Localization And Mapping ◽  
Intrinsic Feature

This is the first chapter of the third section. It describes the kinds of mathematical models usable by a mobile robot to represent its spatial reality, and the reasons by which some of them are more useful than others, depending on the task to be carried out. The most common metric, topological, and hybrid map representations are described from an introductory viewpoint, emphasizing their limitations and advantages for the localization and mapping problems. It then addresses the problem of how to update or build a map from the robot raw sensory data, assuming known robot positions, a situation that becomes an intrinsic feature of some SLAM filters. Since the process greatly depends on the kind of map and sensors, the most common combinations of both are shown.

Probabilistic Bases

2013 ◽  
pp. 60-109
Keyword(s):  
Probability Theory ◽  
Mobile Robot ◽  
Learning Curve ◽  
Bayesian Estimation ◽  
History Of Science ◽  
The Third ◽  
Real Environment ◽  
History Of ◽  
Intrinsic Feature

This is the third chapter of the first section. It is a compendium of all the concepts and theorems of probability theory that are found in the problems of Bayesian estimation of a robot location and the map of its environment. It presents uncertainty as an intrinsic feature of any mobile robot that develops in a real environment. It is then discussed how uncertainty has been treated along the history of science and how probabilistic approaches have represented such a huge success in many engineering fields, including robotics. The fundamental concepts of probability theory are discussed along with some advanced topics needed in further chapters, following a learning curve as smooth and comprehensive as possible.

Using Global Appearance Descriptors to Solve Topological Visual SLAM

2019 ◽  
pp. 1127-1140
Author(s):  
Lorenzo Fernández Rojo ◽  
Luis Paya ◽  
Francisco Amoros ◽  
Oscar Reinoso
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Simultaneous Localization And Mapping ◽  
Visual Slam ◽  
Sources Of Information ◽  
Topological Approach ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Omnidirectional Images

Mobile robots have extended to many different environments, where they have to move autonomously to fulfill an assigned task. With this aim, it is necessary that the robot builds a model of the environment and estimates its position using this model. These two problems are often faced simultaneously. This process is known as SLAM (simultaneous localization and mapping) and is very common since when a robot begins moving in a previously unknown environment it must start generating a model from the scratch while it estimates its position simultaneously. This chapter is focused on the use of computer vision to solve this problem. The main objective is to develop and test an algorithm to solve the SLAM problem using two sources of information: (1) the global appearance of omnidirectional images captured by a camera mounted on the mobile robot and (2) the robot internal odometry. A hybrid metric-topological approach is proposed to solve the SLAM problem.

scholarly journals Method for SLAM Based on Omnidirectional Vision: A Delayed-EKF Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Rodrigo Munguía ◽  
Carlos López-Franco ◽  
Emmanuel Nuño ◽  
Adriana López-Franco
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Real Data ◽  
Omnidirectional Vision ◽  
Wide Field ◽  
Visual Sensor ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Wide Field Of View

This work presents a method for implementing a visual-based simultaneous localization and mapping (SLAM) system using omnidirectional vision data, with application to autonomous mobile robots. In SLAM, a mobile robot operates in an unknown environment using only on-board sensors to simultaneously build a map of its surroundings, which it uses to track its position. The SLAM is perhaps one of the most fundamental problems to solve in robotics to build mobile robots truly autonomous. The visual sensor used in this work is an omnidirectional vision sensor; this sensor provides a wide field of view which is advantageous in a mobile robot in an autonomous navigation task. Since the visual sensor used in this work is monocular, a method to recover the depth of the features is required. To estimate the unknown depth we propose a novel stochastic triangulation technique. The system proposed in this work can be applied to indoor or cluttered environments for performing visual-based navigation when GPS signal is not available. Experiments with synthetic and real data are presented in order to validate the proposal.

scholarly journals Rapid Development of a Mobile Robot for the Nakanoshima Challenge Using a Robot for Intelligent Environments

2020 ◽  
Vol 32 (6) ◽  
pp. 1211-1218
Author(s):  
Tomohiro Umetani ◽  
◽  
Yuya Kondo ◽  
Takuma Tokuda
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Rapid Development ◽  
Field Tests ◽  
Intelligent Environments ◽  
Mobile Platforms ◽  
Robot System ◽  
Location Data ◽  
Localization And Mapping ◽  
Outdoor Environments

Automated mobile platforms are commonly used to provide services for people in an intelligent environment. Data on the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. Therefore, automated mobile robots are required to reconstruct location data in surveillance tasks. This paper describes the development of an autonomous mobile robot to achieve tasks in intelligent environments. In particular, the robot constructed route maps in outdoor environments using laser imaging detection and ranging (LiDAR), and RGB-D sensors via simultaneous localization and mapping. The mobile robot system was developed based on a robot operating system (ROS), reusing existing software. The robot participated in the Nakanoshima Challenge, which is an experimental demonstration test of mobile robots in Osaka, Japan. The results of the experiments and outdoor field tests demonstrate the feasibility of the proposed robot system.

Using Global Appearance Descriptors to Solve Topological Visual SLAM

2018 ◽  
pp. 6894-6905
Author(s):  
Lorenzo Fernández Rojo ◽  
Luis Paya ◽  
Francisco Amoros ◽  
Oscar Reinoso
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Simultaneous Localization And Mapping ◽  
Visual Slam ◽  
Sources Of Information ◽  
Topological Approach ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Omnidirectional Images

Nowadays, mobile robots have extended to many different environments, where they have to move autonomously to fulfill an assigned task. With this aim, it is necessary that the robot builds a model of the environment and estimates its position using this model. These two problems are often faced simultaneously. This process is known as SLAM (Simultaneous Localization and Mapping) and is very common since when a robot begins moving in a previously unknown environment it must start generating a model from the scratch while it estimates its position simultaneously. This work is focused on the use of computer vision to solve this problem. The main objective is to develop and test an algorithm to solve the SLAM problem using two sources of information: (a) the global appearance of omnidirectional images captured by a camera mounted on the mobile robot and (b) the robot internal odometry. A hybrid metric-topological approach is proposed to solve the SLAM problem.

scholarly journals An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4025 ◽  
Author(s):  
Xiaosu Xu ◽  
Xinghua Liu ◽  
Beichen Zhao ◽  
Bo Yang
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Clock Synchronization ◽  
Original System ◽  
Ultra Wideband ◽  
Recursive Least Squares ◽  
Measurement Unit ◽  
Positioning System ◽  
Localization And Mapping ◽  
Initial Attitude

In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in the positioning system are without requirements of communication between UWB anchors and without requirements of clock synchronization of the anchors. By locating the mobile robot using the original system, and then estimating the position of a new anchor using the ranging between the mobile robot and the new anchor, the system can be extended after adding the new anchor into the original system. In an unfamiliar environment (such as fire and other rescue sites), it is able to locate the mobile robot after extending itself. To add the new anchor into the positioning system, a recursive least squares (RLS) approach is used to estimate the position of the new anchor. A maximum correntropy Kalman filter (MCKF) which is based on the maximum correntropy criterion (MCC) is used to fuse data from the UWB network and IMU. The initial attitude of the mobile robot relative to the navigation frame is calculated though comparing position vectors given by a visual simultaneous localization and mapping (SLAM) system and the UWB system respectively. As shown in the experiment section, the root mean square error (RMSE) of the positioning result given by the proposed positioning system with all anchors is 0.130 m. In the unfamiliar environment, the RMSE is 0.131 m which is close to the RMSE (0.137 m) given by the original system with a difference of 0.006 m. Besides, the RMSE based on Euler distance of the new anchor is 0.061 m.

scholarly journals Marker-Based Method for Recognition of Camera Position for Mobile Robots

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1077
Author(s):  
Dong-Gi Gwak ◽  
Kyon-Mo Yang ◽  
Min-Ro Park ◽  
Jehun Hahm ◽  
Jaewan Koo ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Wheeled Mobile Robots ◽  
Robot System ◽  
The Third ◽  
The Core ◽  
Reference Marker ◽  
Internal Parameters ◽  
Camera Position ◽  
Marker Size

Position recognition is one of the core technologies for driving a robot because of differences in environment and rapidly changing situations. This study proposes a strategy for estimating the position of a camera mounted on a mobile robot. The proposed strategy comprises three methods. The first is to directly acquire information (e.g., identification (ID), marker size and marker type) to recognize the position of the camera relative to the marker. The advantage of this marker system is that a combination of markers of different sizes or having different information may be used without having to update the internal parameters of the robot system even if the user frequently changes or adds to the marker’s identification information. In the second, two novel markers are proposed to consider the real environment in which real robots are applied: a nested marker and a hierarchical marker. These markers are proposed to improve the ability of the camera to recognize markers while the camera is moving on the mobile robot. The nested marker is effective for robots like drones, which land and take off vertically with respect to the ground. The hierarchical marker is suitable for robots that move horizontally with respect to the ground such as wheeled mobile robots. The third method is the calculation of the position of an added or moved marker based on a reference marker. This method automatically updates the positions of markers after considering the change in the driving area of the mobile robot. Finally, the proposed methods were validated through experiments.

scholarly journals Contributions to the development of search and rescue mobile robots

2021 ◽  
Vol 343 ◽  
pp. 08016
Author(s):  
Marton Gyarmati ◽  
Mihai Olimpiu Tătar ◽  
Francisc Kadar
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Lessons Learned ◽  
Search And Rescue ◽  
Fourth Section ◽  
The Third ◽  
Locomotion System ◽  
Physical Experiments ◽  
Hybrid Locomotion ◽  
And Control

In this paper the authors present contributions to the development of search and rescue mobile robots. The first part of the paper describes the characteristics of search and rescue field. In the second part the authors presented the development and construction of an experimental prototype focusing on the locomotion systems for the search and rescue field and the results of the physical experiments done and the design and development of a proposed search and rescue mobile robot based on the lessons learned from the experiments. The third part contains the operation and control of the robot. The fourth section presents the simulation of the hybrid locomotion system of the proposed search and rescue mobile robot. The last part of the paper contains the development directions and conclusions.

The KCLBOT: The Challenges of Stereo Vision for a Small Autonomous Mobile Robot

2012 ◽  
Author(s):  
Evangelos Georgiou ◽  
Jian S. Dai ◽  
Michael Luck
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Stereo Vision ◽  
Critical Path ◽  
Vision System ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Sensory Data ◽  
King's College ◽  
Stereo Vision System

In small mobile robot research, autonomous platforms are severely constrained in navigation environments by the limitations of accurate sensory data to preform critical path planning, obstacle avoidance and self-localization tasks. The motivation for this work is to enable small autonomous mobile robots with a local stereo vision system that will provide an accurate reconstruction of a navigation environment for critical navigation tasks. This paper presents the KCLBOT, which was developed in King’s College London’s Centre for Robotic Research and is a small autonomous mobile robot with a stereo vision system.

A Human-Inspired Method for Mobile Robot Navigation

2013 ◽  
Author(s):  
Fatemeh Heidari ◽  
Reza Fotouhi
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Laser Range Finder ◽  
Deadlock Detection ◽  
Sensory Data ◽  
Unstructured Environment ◽  
And Coping

A new method for real-time navigation of mobile robots in complex and mostly unstructured environment is presented. This novel human-inspired method (HIM) uses distance-based sensory data from a laser range finder for real-time navigation of a wheeled mobile robot in unknown and cluttered settings. The approach requires no prior knowledge from the environment and is easy to be implemented for real-time navigation of mobile robots. HIM endows the robot a human-like ability for reasoning about the situations to reach a predefined goal point while avoiding static and moving or unforeseen obstacles; this makes the proposed strategy efficient and effective. Results indicate that HIM is capable of creating smooth (no oscillations) paths for safely navigating the mobile robot, and coping with fluctuating and imprecise sensory data from uncertain environment. HIM specifies the best path ahead, according to the situation of encountered obstacles, preventing the robot to get trapped in deadlock and impassable conditions. This deadlock detection and avoidance is a significant ability of HIM. Also, this algorithm is designed to analyze the environment for detecting both negative and positive obstacles in off-road terrain. The simulation and experimental results of HIM is compared with a fuzzy logic based (FLB) approach.

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