ICRA, May 2002 Simon Thompson & Alexander Zelinsky1 Accurate Local Positioning using Visual Landmarks from a Panoramic Sensor Simon Thompson Alexander Zelinsky Australian National University

ICRA, May 2002 Simon Thompson & Alexander Zelinsky2 Mobile Robot Localisation zLocalise the robot within a known map zGlobal Localisation: localise the robot without apriori knowledge yrequires a search of the entire map zLocal Positioning: maintain a position estimate while moving yrequires an accurate position estimate and appropriate sensors zBoth dependent on the map representation

ICRA, May 2002 Simon Thompson & Alexander Zelinsky3 Map Representations zTopological Map: ysparse representation yefficient globalisation ylimited position accuracy z Metric Map: yfine grained mapping yinefficient global localisation yaccurate local positioning z Sparse representations that allow accurate local positioning

ICRA, May 2002 Simon Thompson & Alexander Zelinsky4 Visual Landmarks for Local Positioning zPlaces represented by sets of Visual Landmarks zVisual Landmarks allow for discrimination between places in global localisation zAutomatic selection of landmark sets zLandmarks must be unique and recognisable zStatic and Dynamic Selection phases zInformation about landmark depth improves accuracy of position estimation

ICRA, May 2002 Simon Thompson & Alexander Zelinsky5 Panoramic Visual Sensor zLarge field of vision zLow resolution

ICRA, May 2002 Simon Thompson & Alexander Zelinsky6 Automatic Selection of Visual Landmarks Static Landmark Selection Dynamic Landmark Selection Panoramic Image Landmark Set “Valley” interest operator Locally unique landmarks Turn Back and Look move (TBL) Template tracking Depth estimation Select reliable landmarks

ICRA, May 2002 Simon Thompson & Alexander Zelinsky7 Landmark Depth Estimation zBearing only Simultaneous Localisation And Mapping (SLAM) over TBL move zKalman Filter to estimate angle and depth of landmarks and the position of the robot during the TBL move zEstimating uncertainty in estimates as well System state: Robot position, initial estimates of depth and angle Observed angle to landmark Jacobian relating changes in observations to changes in state

ICRA, May 2002 Simon Thompson & Alexander Zelinsky8 Landmark Depth Estimation Results Simulation (Actual, and estimates after 1, 200, 400 steps: movement + observation) Noise: +/- 1 degree in observations 10% in odometry

ICRA, May 2002 Simon Thompson & Alexander Zelinsky9 Real World Results Landmarks Estimated landmark positions Visual landmarks can make it hard to verify estimates

ICRA, May 2002 Simon Thompson & Alexander Zelinsky10 Artificial Landmarks z+/- 10cm accuracy in landmark depth estimation

ICRA, May 2002 Simon Thompson & Alexander Zelinsky11 Artificial Landmarks Result

ICRA, May 2002 Simon Thompson & Alexander Zelinsky12 Local Positioning within Places Panoramic Image Position Estimate Locate Landmarks Reselect Predict Measure Particle Filter Template Matching Each Particle represents a pose [x,y,  ] Particle set  PDF of Poses Reselect most likely particles Apply motion model to particles: deterministic drift, stochastic noise Likelihood of current observation given a particle’s pose Intersection of landmark observation and estimated position uncertainty regions zRobot Motion

ICRA, May 2002 Simon Thompson & Alexander Zelinsky13 Sensor Model for Local Positioning Evaluate the probability of each particle given the current observation  Sensor Model Apply sensor model over range of all Possible poses Sample Particle 16 landmarks 1 landmark

ICRA, May 2002 Simon Thompson & Alexander Zelinsky14 Local Positioning Results Results : Actual Path Heuristic EstimationProbabilistic Estimation zEstimate robot position over a TBL movement zHeuristic Approach - expansion/contraction of landmark pairs zProbabilistic approach: particle filter: +/- 50mm, equivalent to metric approach within places

ICRA, May 2002 Simon Thompson & Alexander Zelinsky15 Global Localisation zDo the sets of visual landmarks uniquely identify a place?  Place Discrimination zFor each place locate landmarks throughout path zComputationally expensive

ICRA, May 2002 Simon Thompson & Alexander Zelinsky16 Place Discrimination zDiscriminate 15 places over the approx. 700 images in the path zLandmark set recognition performance

ICRA, May 2002 Simon Thompson & Alexander Zelinsky17 Place Discrimination (Cont)

ICRA, May 2002 Simon Thompson & Alexander Zelinsky18 Localisation in a Topological Map zConstruct map with places connected by transitions: direction, distance zGlobal localisation by place discrimination zLocal positioning within places zPass local position between places using transition information zPosition tracking possible but problems in Condensation algorithm exist

ICRA, May 2002 Simon Thompson & Alexander Zelinsky19 Summary zPlaces in a topological map represented by sets of visual landmarks zLandmarks are automatically selected using a static and dynamic selection process zDepth of landmarks estimated using bearing only slam zLandmark depth information allows for accurate local positioning equivalent to metric maps zVisual landmarks still allow for place discrimination zLocalisation in a topological map possible zMore experimentation is underway