000 07044cam a2200445 a 4500
003 EG-NbEJU
005 20240610124754.0
008 190424s2011 maua bi 001 0 eng d
010 _a2010028053
016 7 _a015690249
_2Uk
020 _a9780262015356 (hardcover : alk. paper)
020 _a0262015358 (hardcover : alk. paper)
035 _a(OCoLC)ocn649700153
040 _aEG-NbEJU
_cEG-NbEJU
_dEG-NbEJU
_beng
041 _aeng
042 _apcc
050 0 0 _aTJ211.415
_b.S54 2011
100 1 _aSiegwart , Roland
245 1 0 _aIntroduction to autonomous mobile robots /
_cRoland Siegwart , Illah R. Nourbakhsh , and Davide Scaramuzza
250 _aSecond edition
260 _a.Cambridge , Mass :
_bMIT Press ,
_c2011
300 _axvi, 453 pages :
_billustrations ;
_c24 cm
490 1 _aIntelligent robotics and autonomous agents
504 _aIncludes bibliographical references and index
505 0 0 _gMachine generated contents note :
_g1.
_tIntroduction --
_g1.1.
_tIntroduction --
_g1.2.
_tAn Overview of the Book --
_g2.
_tLocomotion --
_g2.1.
_tIntroduction --
_g2.1.1.
_tKey issues for locomotion --
_g2.2.
_tLegged Mobile Robots --
_g2.2.1.
_tLeg configurations and stability --
_g2.2.2.
_tConsideration of dynamics --
_g2.2.3.
_tExamples of legged robot locomotion --
_g2.3.
_tWheeled Mobile Robots --
_g2.3.1.
_tWheeled locomotion: The design space --
_g2.3.2.
_tWheeled locomotion: Case studies --
_g2.4.
_tAerial Mobile Robots --
_g2.4.1.
_tIntroduction --
_g2.4.2.
_tAircraft configurations --
_g2.4.3.
_tState of the art in autonomous VTOL --
_g2.5.
_tProblems --
_g3.
_tMobile Robot Kinematics --
_g3.1.
_tIntroduction --
_g3.2.
_tKinematic Models and Constraints --
_g3.2.1.
_tRepresenting robot position --
_g3.2.2.
_tForward kinematic models --
_g3.2.3.
_tWheel kinematic constraints --
_g3.2.4.
_tRobot kinematic constraints --
_tExamples: Robot kinematic models and constraints
505 0 0 _g3.3.
_tMobile Robot Maneuverability --
_g3.3.1.
_tDegree of mobility --
_g3.3.2.
_tDegree of steerability --
_g3.3.3.
_tRobot maneuverability --
_g3.4.
_tMobile Robot Workspace --
_g3.4.1.
_tDegrees of freedom --
_g3.4.2.
_tHolonomic robots --
_g3.4.3.
_tPath and trajectory considerations --
_g3.5.
_tBeyond Basic Kinematics --
_g3.6.
_tMotion Control (Kinematic Control) --
_g3.6.1.
_tOpen loop control (trajectory-following) --
_g3.6.2.
_tFeedback control --
_g3.7.
_tProblems --
_g4.
_tPerception --
_g4.1.
_tSensors for Mobile Robots --
_g4.1.1.
_tSensor classification --
_g4.1.2.
_tCharacterizing sensor performance --
_g4.1.3.
_tRepresenting uncertainty --
_g4.1.4.
_tWheel/motor sensors --
_g4.1.5.
_tHeading sensors --
_g4.1.6.
_tAccelerometers --
_g4.1.7.
_tInertial measurement unit (IMU) --
_g4.1.8.
_tGround beacons --
_g4.1.9.
_tActive ranging --
_g4.1.10.
_tMotion/speed sensors --
_g4.1.11.
_tVision sensors --
_g4.2.
_tFundamentals of Computer Vision --
_g4.2.1.
_tIntroduction --
_g4.2.2.
_tThe digital camera --
_g4.2.3.
_tImage formation --
_g4.2.4.
_tOmnidirectional cameras
505 0 0 _g4.2.5.
_tStructure from stereo --
_g4.2.6.
_tStructure from motion --
_g4.2.7.
_tMotion and optical flow --
_g4.2.8.
_tColor tracking --
_g4.3.
_tFundamentals of Image Processing --
_g4.3.1.
_tImage filtering --
_g4.3.2.
_tEdge detection --
_g4.3.3.
_tComputing image similarity --
_g4.4.
_tFeature Extraction --
_g4.5.
_tImage Feature Extraction: Interest Point Detectors --
_g4.5.1.
_tIntroduction --
_g4.5.2.
_tProperties of the ideal feature detector --
_g4.5.3.
_tCorner detectors --
_g4.5.4.
_tInvariance to photometric and geometric changes --
_g4.5.5.
_tBlob detectors --
_g4.6.
_tPlace Recognition --
_g4.6.1.
_tIntroduction --
_g4.6.2.
_tFrom bag of features to visual words --
_g4.6.3.
_tEfficient location recognition by using an inverted file --
_g4.6.4.
_tGeometric verification for robust place recognition --
_g4.6.5.
_tApplications --
_g4.6.6.
_tOther image representations for place recognition --
_g4.7.
_tFeature Extraction Based on Range Data (Laser, Ultrasonic) --
_g4.7.1.
_tLine fitting --
_g4.7.2.
_tSix line-extraction algorithms
505 0 0 _g4.7.3.
_tRange histogram features --
_g4.7.4.
_tExtracting other geometric features --
_g4.8.
_tProblems --
_g5.
_tMobile Robot Localization --
_g5.1.
_tIntroduction --
_g5.2.
_tThe Challenge of Localization: Noise and Aliasing --
_g5.2.1.
_tSensor noise --
_g5.2.2.
_tSensor aliasing --
_g5.2.3.
_tEffector noise --
_g5.2.4.
_tAn error model for odometric position estimation --
_g5.3.
_tTo Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions --
_g5.4.
_tBelief Representation --
_g5.4.1.
_tSingle-hypothesis belief --
_g5.4.2.
_tMultiple-hypothesis belief --
_g5.5.
_tMap Representation --
_g5.5.1.
_tContinuous representations --
_g5.5.2.
_tDecomposition strategies --
_g5.5.3.
_tState of the art: Current challenges in map representation --
_g5.6.
_tProbabilistic Map-Based Localization --
_g5.6.1.
_tIntroduction --
_g5.6.2.
_tThe robot localization problem --
_g5.6.3.
_tBasic concepts of probability theory --
_g5.6.4.
_tTerminology --
_g5.6.5.
_tThe ingredients of probabilistic map-based localization
505 0 0 _g5.6.6.
_tClassification of localization problems --
_g5.6.7.
_tMarkov localization --
_g5.6.8.
_tKalman filter localization --
_g5.7.
_tOther Examples of Localization Systems --
_g5.7.1.
_tLandmark-based navigation --
_g5.7.2.
_tGlobally unique localization --
_g5.7.3.
_tPositioning beacon systems --
_g5.7.4.
_tRoute-based localization --
_g5.8.
_tAutonomous Map Building --
_g5.8.1.
_tIntroduction --
_g5.8.2.
_tSLAM: The simultaneous localization and mapping problem --
_g5.8.3.
_tMathematical definition of SLAM --
_g5.8.4.
_tExtended Kalman Filter (EKF) SLAM --
_g5.8.5.
_tVisual SLAM with a single camera --
_g5.8.6.
_tDiscussion on EKF SLAM --
_g5.8.7.
_tGraph-based SLAM --
_g5.8.8.
_tParticle filter SLAM --
_g5.8.9.
_tOpen challenges in SLAM --
_g5.8.10.
_tOpen source SLAM software and other resources --
_g5.9.
_tProblems --
_g6.
_tPlanning and Navigation --
_g6.1.
_tIntroduction --
_g6.2.
_tCompetences for Navigation: Planning and Reacting --
_g6.3.
_tPath Planning --
_g6.3.1.
_tGraph search --
_g6.3.2.
_tPotential field path planning
505 0 0 _g6.4.
_tObstacle avoidance --
_g6.4.1.
_tBug algorithm --
_g6.4.2.
_tVector field histogram --
_g6.4.3.
_tThe bubble band technique --
_g6.4.4.
_tCurvature velocity techniques --
_g6.4.5.
_tDynamic window approaches --
_g6.4.6.
_tThe Schlegel approach to obstacle avoidance --
_g6.4.7.
_tNearness diagram --
_g6.4.8.
_tGradient method --
_g6.4.9.
_tAdding dynamic constraints --
_g6.4.10.
_tOther approaches --
_g6.4.11.
_tOverview --
_g6.5.
_tNavigation Architectures --
_g6.5.1.
_tModularity for code reuse and sharing --
_g6.5.2.
_tControl localization --
_g6.5.3.
_tTechniques for decomposition --
_g6.5.4.
_tCase studies: tiered robot architectures --
_g6.6.
_tProblems --
_tBibliography --
_tBooks --
_tPapers --
_tReferenced Webpages.
650 7 _aMobile robots
_2LCSH
650 7 _aAutonomous robots
_2LCSH
650 7 _aRobots , Industrial
_2LCSH
700 1 _aNourbakhsh , Illah Reza
_d1970-
_eContributing auther
700 1 _aScaramuzza , Davide
_eContributing auther
901 _aHaGeR
902 _aENG_03_ (2505)
902 _aENG_03_(2485)
942 _2lcc
_cBK
999 _c3342
_d3342