Map-Building and Localization by Three-Dimensional Local Features for Ubiquitous Service Robot

2007 ◽  
pp. 69-79 ◽  
Author(s):  
Youngbin Park ◽  
Seungdo Jeong ◽  
Il Hong Suh ◽  
Byung-Uk Choi
Keyword(s):  
Three Dimensional ◽  
Local Features ◽  
Service Robot ◽  
Map Building

scholarly journals Use of Gradient-Based Shadow Detection for Estimating Environmental Illumination Distribution

2018 ◽  
Vol 8 (11) ◽  
pp. 2255 ◽  
Author(s):  
Sangyoon Lee ◽  
Hyunki Hong
Keyword(s):  
Three Dimensional ◽  
Local Features ◽  
Shadow Detection ◽  
Reference Object ◽  
Light Sources ◽  
Illumination Estimation ◽  
Shadow Boundary ◽  
Sampling Position ◽  
Gradient Based ◽  
Edge Based

Environmental illumination information is necessary to achieve a consistent integration of virtual objects in a given image. In this paper, we present a gradient-based shadow detection method for estimating the environmental illumination distribution of a given scene, in which a three-dimensional (3-D) augmented reality (AR) marker, a cubic reference object of a known size, is employed. The geometric elements (the corners and sides) of the AR marker constitute the candidate’s shadow boundary; they are obtained on a flat surface according to the relationship between the camera and the candidate’s light sources. We can then extract the shadow regions by collecting the local features that support the candidate’s shadow boundary in the image. To further verify the shadows passed by the local features-based matching, we examine whether significant brightness changes occurred in the intersection region between the shadows. Our proposed method can reduce the unwanted effects caused by the threshold values during edge-based shadow detection, as well as those caused by the sampling position during point-based illumination estimation.

Object detection and mapping for service robot tasks

Robotica ◽  
2007 ◽  
Vol 25 (2) ◽  
pp. 175-187 ◽  
Author(s):  
Staffan Ekvall ◽  
Danica Kragic ◽  
Patric Jensfelt
Keyword(s):  
Object Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Semantic Knowledge ◽  
Service Robot ◽  
Map Building ◽  
Extensive Evaluation ◽  
Localization And Mapping ◽  
Different Types ◽  
Robot Tasks

SUMMARYThe problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

1A1-D16 Three-dimensional map building for forest structure analysis using SOKUIKI sensor

2010 ◽  
Vol 2010 (0) ◽  
pp. _1A1-D16_1-_1A1-D16_4 ◽  
Author(s):  
Shuhei KONDOU ◽  
Keiko SHIOZAWA ◽  
Takashi TSUBOUCHI ◽  
Shuhei TOMIMURA ◽  
Akiko MOCHIZUKI ◽  
...  
Keyword(s):  
Structure Analysis ◽  
Forest Structure ◽  
Three Dimensional ◽  
Map Building

1A1-E15 Three dimensional map building by scan matching with two laser range sensors

2009 ◽  
Vol 2009 (0) ◽  
pp. _1A1-E15_1-_1A1-E15_3
Author(s):  
Yuji MIYAUCHI ◽  
Takashi Ogino ◽  
Toshinari Akimoto ◽  
Akihiro Matsumoto
Keyword(s):  
Three Dimensional ◽  
Map Building ◽  
Scan Matching ◽  
Laser Range ◽  
Range Sensors

Three-Dimensional Map Generation From Side-Scan Sonar Images

1990 ◽  
Vol 112 (2) ◽  
pp. 96-102 ◽  
Author(s):  
J. M. Cuschieri ◽  
M. Hebert
Keyword(s):  
Remote Sensing ◽  
Three Dimensional ◽  
Map Building ◽  
Side Scan Sonar ◽  
Essential Components ◽  
Map Generation ◽  
Sonar Systems ◽  
Sonar Images ◽  
Direct Generation

The generation of three-dimensional (3-D) images and map building are essential components in the development of an autonomous underwater system. Although the direct generation of 3-D images is more efficient than the recovery of 3-D data from 2-D information, at present for underwater applications where sonar is the main form of remote sensing, the generation of 3-D images can only be achieved by either complex sonar systems or with systems which have a rather low resolution. In this paper an overview is presented on the type of sonar systems that are available for underwater remote sensing, and then a technique is presented which demonstrates how through simple geometric reasoning procedures, 3-D information can be recovered from side scan-type (2-D) data. Also presented is the procedure to perform map building on the estimated 3-D data.

Map Building in Indoor Environment for Service Robot Based on Laser Sensor

2012 ◽  
Vol 461 ◽  
pp. 671-676
Author(s):  
Long Hui Wu ◽  
Shi Gang Cui ◽  
Li Zhao
Keyword(s):  
Autonomous Navigation ◽  
Indoor Environment ◽  
Service Robot ◽  
Laser Sensor ◽  
Uncertain Information ◽  
Map Building ◽  
Environment Map ◽  
Map Updating ◽  
Fitting In ◽  
Local Map

Map building is an essential problem in the field of mobile robot research. The accurate environmental map provides the important safeguard for the robot autonomous navigation and localization. In this paper, the two-dimensional environment map based on geometric features information was built by laser data when service robot worked in indoor structured environment. However, this article focused on analyzing the method of line fitting in the process of building local map and the method of global map updating. The experiment indicated that this method has the high accuracy and effectiveness, also reduces error caused by the uncertain information in the process of map building, which makes the indoor environment map update in real time.

Simultaneous Localization and Mapping with Identification of Landmarks Based on Monocular Vision

2011 ◽  
Vol 366 ◽  
pp. 90-94
Author(s):  
Ying Min YI ◽  
Yu Hui
Keyword(s):  
Three Dimensional ◽  
Simultaneous Localization And Mapping ◽  
Active Vision ◽  
Monocular Vision ◽  
Observation Data ◽  
Map Building ◽  
Environmental Objects ◽  
Localization And Mapping ◽  
Vision Algorithm ◽  
Three Dimensional Vision

How to identify objects is a hot issue of robot simultaneous localization and mapping (SLAM) with monocular vision. In this paper, an algorithm of wheeled robot’s simultaneous localization and mapping with identification of landmarks based on monocular vision is proposed. In observation steps, identifying landmarks and locating position are performed by image processing and analyzing, which converts vision image projection of wheeled robots and geometrical relations of spatial objects into calculating robots’ relative landmarks distance and angle. The integral algorithm procedure follows the recursive order of prediction, observation, data association, update, mapping to have simultaneous localization and map building. Compared with Active Vision algorithm, Three dimensional vision and stereo vision algorithm, the proposed algorithm is able to identify environmental objects and conduct smooth movement as well.

Sign in / Sign up

Export Citation Format

Share Document