Visual Localization for Mobile Robots Based on Composite Map

2013 ◽  
Vol 25 (1) ◽  
pp. 25-37 ◽  
Author(s):  
Hung-Hsiu Yu ◽  
◽  
Hsiang-Wen Hsieh ◽  
Yu-Kuen Tasi ◽  
Zhi-Hung Ou ◽  
...  
Keyword(s):  
Salient Feature ◽  
Visual Localization ◽  
Laser Range Finder ◽  
Range Finder ◽  
Feature Points ◽  
Robot System ◽  
Laser Range ◽  
Depth Data ◽  
Map Construction ◽  
Distinct Features

In this paper, we propose a novel mobile robot visual localization method consisting of two processing stages: map construction and visual localization. In the map construction stage, both laser range finder and camera are used to construct a composite map. Depth data are collected from laser range finder while distinct features of salient feature points are gathered from camera provided images. In the visual localization stage, only camera is used and the robot system detects feature points from camera provided images, computes features of the detected feature points, matches them with the features recorded in previously constructed composite map, and decides location of the robot. Using this method, a robot can locate its own position effectively without expensive laser range finder so that greater acceptance can be expected due to affordability. With the proposedmethod, several experiments have been performed. The matching accuracy of proposed feature extraction achieves 97.79%, compared with 92.96% of SURF. Experiment results show that our method not only reduces hardware cost of robot localization, but also offers high accuracy.

A Novel Method for Perception and Map Construction of Environment Information Based on Laser Range Finder

2013 ◽  
Vol 347-350 ◽  
pp. 307-311
Author(s):  
Wen Jie Zhu ◽  
Guang Long Wang ◽  
Feng Qi Gao ◽  
Zhong Tao Qiao ◽  
Ji Chen Li
Keyword(s):  
Communication Protocol ◽  
Measurement Data ◽  
Laser Range Finder ◽  
Visualization Method ◽  
Range Finder ◽  
Laser Range ◽  
Range Finding ◽  
Map Construction ◽  
Working Principle ◽  
Novel Method

A method for perception and map construction of the unknown environmental information is described in this paper by analyzing and processing the data measured by laser range finder (LRF). The structure of the laser range finder, the structure of its returned measurement data, its working principle, and communication protocol are presented. The physical meaning of the data are studied and illustrated as well as its representation methods. The corresponding algorithm routine and the GUI interface program are introduced. Experiments are conducted to verify the range finding accuracy of the sensor and the data visualization method is also introduced. Results indicate that the proposed method is sufficiently valid and can satisfy the desired requirements.

VISION-BASED HUMANOID NAVIGATION USING SELF-SUPERVISED OBSTACLE DETECTION

2013 ◽  
Vol 10 (02) ◽  
pp. 1350016 ◽  
Author(s):  
DANIEL MAIER ◽  
CYRILL STACHNISS ◽  
MAREN BENNEWITZ
Keyword(s):  
Real World ◽  
Humanoid Robots ◽  
Obstacle Detection ◽  
Laser Range Finder ◽  
Range Finder ◽  
Laser Range ◽  
Efficient Approach ◽  
Depth Data ◽  
Texture Information ◽  
Laser Data

In this paper, we present an efficient approach to obstacle detection for humanoid robots based on monocular images and sparse laser data. We particularly consider collision-free navigation with the Nao humanoid, which is the most popular small-size robot nowadays. Our approach first analyzes the scene around the robot by acquiring data from a laser range finder installed in the head. Then, it uses the knowledge about obstacles identified in the laser data to train visual classifiers based on color and texture information in a self-supervised way. While the robot is walking, it applies the learned classifiers to the camera images to decide which areas are traversable. As we show in the experiments, our technique allows for safe and efficient humanoid navigation in real-world environments, even in the case of robots equipped with low-end hardware such as the Nao, which has not been achieved before. Furthermore, we illustrate that our system is generally applicable and can also support the traversability estimation using other combinations of camera and depth data, e.g. from a Kinect-like sensor.

Development of Mobile Robot System Equipped with Camera and Laser Range Finder Realizing HOG-Based Person Following and Autonomous Returning

2014 ◽  
Vol 26 (1) ◽  
pp. 68-77 ◽  
Author(s):  
Masashi Awai ◽  
◽  
Atsushi Yamashita ◽  
Takahito Shimizu ◽  
Toru Kaneko ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Potential Method ◽  
Range Data ◽  
Laser Range Finder ◽  
Range Finder ◽  
Color Information ◽  
Robot System ◽  
Laser Range ◽  
Person Following ◽  
Starting Point

In this paper, we propose a mobile robot system which has functions of person following and autonomous returning. The robot realizes these functions by analyzing information obtained with camera and laser range finder. Person following is performed by using HOG features, color information, and pattern of range data. Along with person following, a map of the ambient environment is generated from range data. Autonomous returning to the starting point is performed by applying potential method to the generated map. We verified the proposed method by experiment using a wheel mobile robot in an indoor environment.

Computationally Efficient Mapping for a Mobile Robot with a Downsampling Method for the Iterative Closest Point

2018 ◽  
Vol 30 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Shodai Deguchi ◽  
◽  
Genya Ishigami
Keyword(s):  
Mobile Robot ◽  
Point Cloud ◽  
Iterative Closest Point ◽  
Laser Range Finder ◽  
Range Finder ◽  
Point Cloud Data ◽  
Feature Points ◽  
Computationally Efficient ◽  
Laser Range ◽  
Cloud Data

This paper proposes a computationally efficient method for generating a three-dimensional environment map and estimating robot position. The proposed method assumes that a laser range finder mounted on a mobile robot can be used to provide a set of point cloud data of an environment around the mobile robot. The proposed method then extracts typical feature points from the point cloud data using an intensity image taken by the laser range finder. Subsequently, feature points extracted from two or more different sets of point cloud data are correlated by the iterative closest point algorithm that matches the points between the sets, creating a large map of the environment as well as estimating robot location in the map. The proposed method maintains an accuracy of the mapping while reducing the computational cost by downsampling the points used for the iterative closest point. An experimental demonstration using a mobile robot test bed confirms the usefulness of the proposed method.

scholarly journals Realization of a Robot System That Plays “Darumasan-Ga-Koronda ” Game with Humans

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 55 ◽  
Author(s):  
Yutaka Hiroi ◽  
Akinori Ito
Keyword(s):  
Experimental Result ◽  
Original Position ◽  
Total System ◽  
Laser Range Finder ◽  
Range Finder ◽  
Game Play ◽  
Robot System ◽  
Laser Range ◽  
Track Record

In this paper, we describe the development of a robot that plays the “darumasan-ga- koronda” game with human players. It is a big challenge to develop a robot that can play with human players. We first observed the human–human game play and listed the problems for a robot to play the game with human players. We pointed out the following six tasks: detect players, track the detected players, decide if players are freezing (the “out” judgment), move to the nearest player, touch the player, and move back to the original position. Then, we designed a robot that uses a laser range finder (LRF) to measure the players and a robot avatar to interact with the players. Then, we developed the components needed to play the game. In this paper, we describe the details of the development of the arm to touch the player, the “out” judgment, and a function to return to the origin after finishing the game. Then, we describe an experimental result of the evaluation of the total system, as well as the track record of the demonstrations in the events.

Analysis of Pitting Corrosion for 2219 Aluminium Alloy under Different Corrosion Circumstances

2020 ◽  
Vol 1007 ◽  
pp. 160-164
Author(s):  
Yi Fei Li ◽  
Sheng Li Lv
Keyword(s):  
Statistical Analysis ◽  
Aluminium Alloy ◽  
Immersion Time ◽  
Laser Range Finder ◽  
Range Finder ◽  
Corrosion Depth ◽  
Laser Range ◽  
Depth Data ◽  
Corrosion Pits ◽  
The Relationship

According to the observation of a metallurgical microscope, surface morphology of 2219 aluminium alloy under several corrosion circumstances, such as corrosion pits and grain boundary corrosion, is directly perceived. Furthermore, with a laser range finder, corrosion depth data can be measured, and by using some methods of data processing, the affection for this material of certain solution components and immersion time is studied quantitatively. This binary study mean not only provides both graphical and statistical analysis, but also gives the relationship between them, which makes the result more reliable.

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