Dissertation > Excellent graduate degree dissertation topics show

Research on Vision Based Mapping and Localization for Mobile Robot in Unknown Environments

Author: WangZuo
Tutor: CaiZiXing
School: Central South University
Course: Applied Computer Technology
Keywords: unknown environments incremental topological mapping and localization visual saliency hidden Markov model moving object filtering vision based navigation system
CLC: TP242
Type: PhD thesis
Year: 2007
Downloads: 1467
Quote: 10
Read: Download Dissertation


The thesis was supported by NSFC key project "Research on theoriesand methods for navigation control of mobile robots under unknownenvironments"(60234030). Related works was subject to the part "mappingand localization" of the project. The thesis focuses on the problems ofvision(camera) based topological mapping、localization and navigation ofmobile robot in unknown environments. The objectives are exploring newapproaches of vision based mapping and localization in unknownenvironments, improving the accuracy of created topological map,decreasing the storage requirement of map, advancing the precision ofself-localization and ability of long-time surviving of mobile robot inunknown environments.It is absolutely necessary for vision based mapping and localizationthat images are analyzed to obtain features of environments. The thesisfirstly researches the approaches of natural landmark detection andrepresentation in unknown environments from bottom to up based on visualsaliency. A saliency detection model with feedback mechanism is presented.An opponency operator among scales is defined, which consideres theinfluence of global information. So the opponencies of color and textureamong multi-scales are computed and combined to obtain the saliency mappointing out candidate natural landmark’s position. By the feedback, thecontribution of each feature to image analysis on different scenes arecontrolled. Experiments such as basic performance test and stability testand anti-jamming test show that the approach has excellent repeatability ofsalient position detection. Then LOG operator is applied to selectappropriate size automatically to create salient region as natural landmarkprototype. The features involving gradient orientation and moment andcanonical hue are used to represent natural landmarks. Experiments ofobject recognition show that the natural landmark based on local salientregion has high stability and tolerance of image diversity caused by scaleand viewpoint etc changed. The accuracy of recognition is higher.After the natural landmarks are extracted, an incremental vision topological mapping and localization approach is presented based onhidden Markov model (HMM). Firstly a single CCD camera is drivenscanning current environment to obtain omni image sequence. While thesalient landmarks are detected from these images, HMM is used to modelthe space relationship among these landmarks and create a topological node.So localization problem can be transformed to be the evaluation problem ofHMM. A initial localization method based on joint probability distributionis designed. A leaming strategy based on maximum a posteriori (MAP) isalso designed to deal with uncertainty of localization. The approach hassome characteristics as follows. Local salient image features replace wholeimage features to contribute for environment recognition. And HMM isused to capture the space relationship among them. So localization (placerecognition) has more tolerance of environment change and more trustiness.The state space of HMM is invariable with the scale of exploringenvironments increasing, which decreases the computation requirement ofprobabilistic localization. The approach supports online incrementaltopologically mapping and locating simultaneously. While most visionbased topological mapping and localization systems have two distinctivestages of training for mapping offline and locating online. Experimentsshow that the approaches can improve effectively the accuracy of scenerecognition and realize online incremental topological mapping andlocalization.To improve the applicability of vision based mapping and localizationapproach in dynamic natural environments, the thesis studies the problemof topological mapping in dynamic unknown environments existingmoving objects. The objective is to eliminate the influence of movingobjects on mapping and improve the accuracy of topological map. Firstly amoving objects detection method with movement compensation ispresented. Considering the integrality of detection and tracking task, afeedback control system for driving the camera is built based on Kalmanfilter. To extract integral object fast, a modified fuzzy C-Means clusteringmethod is presented. Based on these the strategy of extracting naturallandmarks is modified as: classifying all detected landmarks as static landmarks and dynamic landmarks, then abandoning those dynamiclandmarks to avoid the influence of moving objects. Experiments show thatthe approach can filter noisy landmarks and improve the precision oftopological mapping and localization.Founded by the research of natural landmark extraction andincremental mapping and dynamic object detection, a vision based onlinetopological mapping and navigation system (VOTMNS) is presented andimplemented on mobile robot MORCS-1. The system includes 4 part ofnatural landmark extraction, mapping and localization, management of mapand landmark library, planning. In terms of evaluation of usability, amanagement method of landmark library based on competitive learning ispresented. Compared with the method updating all landmark’s existingstate, this method has more efficiency and fewer computation cost. Amanagement method of uncertainty in navigation is designed based oninitial localization, which make robot plan again when locates to a node notbelonging to the planning path during navigation. Experiments show thatthe system has the ability of mapping stably and navigating safely in flatunknown environments. Landmark library management and the timeperformance of the system indicate that the system has the ability ofreal-time work.

Related Dissertations

  1. Packet Loss Recovering Technology for Speech Transmission over Network,TN912.3
  2. Research on Domain Entity Attribute and Event Extraction Technology,TP391.1
  3. Multi-threaded fusion soccer video semantic analysis and event detection,TP391.41
  4. Chinese Speech Synthesis System Improvement and Implementation,TN912.33
  5. Research on Characteristic Analysis and Recognition Algorithm of Heart Sound Signal,R318.04
  6. Extended Hidden Markov Models and Parameter Estimation Based on Genetic Algorithm,O211.62
  7. Research of Multi-Sensory Myoelectric Prosthetic Hand with Hardness and Thermal Conductivity,TP242
  8. A Research on Chinese Word Segmentation Based on Phonetic Annotation,TP391.1
  9. Research on DBN-Based Continuous Speech Recognition,TN912.34
  10. Research on the Key Technologies fo Speech Recognition for Robot Communication,TN912.34
  11. The LVCSR system based on adaptive methods of semi-supervised learning,TN912.34
  12. Some Strong Laws for Markov Chain Fields Indexed by a Nonhomogeneous Tree of Module M,O211.62
  13. Research on Automatic Notation of Word for Tibetan Corpus Based on HMM,H214
  14. Research on Information Awareness Technology Oriented to Cognitive Networks,TP393.02
  15. Research and Implementation on Community Discovery from Network Based on Data Mining,TP393.094
  16. Statistical Image Modeling and Image Segmentation in Contourlet Domain,TP391.41
  17. Event Detection Modeling and Optimization in Intelligent Video Surveillance,TP391.41
  18. Research on Virtual Human Motion Synthesis Techniques and Engineering Application,TP391.41
  19. Research of Segmentation Based Chinese Continuous Speech Recognition Technology,TN912.34
  20. Prediction of Stock Price Based on Hidden Markov Model,F830.91
  21. The Research of Compliance Testing Technology of Traffic Terminology and Standards,TP391.1

CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Robotics > Robot
© 2012 www.DissertationTopic.Net  Mobile