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.

Landmark detection and localization for mobile robot applications: a multisensor approach

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 663-673 ◽  
Author(s):  
Dilan Amarasinghe ◽  
George K. I. Mann ◽  
Raymond G. Gosine
Keyword(s):  
The Other ◽  
Laser Range Finder ◽  
Fusion Method ◽  
Range Finder ◽  
Landmark Detection ◽  
Laser Range ◽  
Localization And Mapping ◽  
Laser Data ◽  
Single Sensor ◽  
Detection And Localization

SUMMARYThis paper describes a landmark detection and localization using an integrated laser-camera sensor. Laser range finder can be used to detect landmarks that are direction invariant in the laser data such as protruding edges in walls, edges of tables, and chairs. When such features are unavailable, the dependant processes will fail to function. However, in many instances, larger number of landmarks can be detected using computer vision. In the proposed method, camera is used to detect landmarks while the location of the landmark is measured by the laser range finder using laser-camera calibration information. Thus, the proposed method exploits the beneficial aspects of each sensor to overcome the disadvantages of the other sensor. While highlighting the drawbacks and limitations of single sensor based methods, an experimental results and important statistics are provided for the verification of the affectiveness sensor fusion method using Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) as an example application.

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.

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.

A Touch-Based Wheelchair Control Interface: Concept and Implementation

2013 ◽  
Vol 300-301 ◽  
pp. 1471-1474 ◽  
Author(s):  
Ting Shuo Chen ◽  
Yu Chen Kuo ◽  
Yao Sheng Syu ◽  
Chung Hsien Kuo
Keyword(s):  
Obstacle Detection ◽  
Artificial Potential Field ◽  
Laser Range Finder ◽  
Range Finder ◽  
Reactive Navigation ◽  
Laser Range ◽  
Field Approach ◽  
Intelligent Wheelchair ◽  
Control Interface ◽  
Wheelchair Control

In this paper, we propose a wTouch interface for controlling a human-centered intelligent wheelchair. The wTouch interface is a touch panel which performs similar joystick manipulations while it provides convenient wheelchair control interface, as well as offers useful wheelchair status. In addition, reactive navigation techniques are also developed by combining with the obstacle information to perform collision free navigations. The obstacle detection sensor uses a laser range finder and the reactive navigation uses the artificial potential field approach. Finally, a wTouch wheelchair prototype is produced in our laboratory for evaluating the performance of reactive navigations.

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