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CSE-473. Mobile Robot Mapping. Mapping with Raw Odometry. Graphical Model of Mapping. u. u. u. 0. 1. t-1. x. x. x. x. 0. 1. 2. t. m. z. z. z. 1. 2. t. Graph-SLAM Idea. Robot Poses and Scans [Lu and Milios 1997]. Successive robot poses connected by odometry
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CSE-473 Mobile Robot Mapping
Graphical Model of Mapping u u u 0 1 t-1 ... x x x x 0 1 2 t m z z z 1 2 t
Robot Poses and Scans [Lu and Milios 1997] • Successive robot poses connected by odometry • Sensor readings yield constraints between poses • Constraints represented by Gaussians • Globally optimal estimate
Loop Closure • Use scan patches to detect loop closure • Add new position constraints • Deform the network based on covariances of matches Before loop closure After loop closure
3D Outdoor Mapping 108 features, 105 poses, only few secs using cg.
Rao-Blackwellized Mapping Compute a posterior over the map and possible trajectories of the robot : map and trajectory measurements map robot motion trajectory
Particle #1 x, y, x, y, x, y, x, y, Landmark 1 Landmark 1 Landmark 1 Landmark 1 Landmark 2 Landmark 2 Landmark 2 Landmark 2 Landmark N Landmark N Landmark N Landmark N … … … … Particle #2 Particle #3 FastSLAM Robot Pose 2 x 2 Kalman Filters … Particle M [Begin courtesy of Mike Montemerlo]
Example map of particle 3 map of particle 1 map of particle 2 3 particles
Rao-Blackwellized Mapping with Scan-Matching Map: Intel Research Lab Seattle Loop Closure
Rao-Blackwellized Mapping with Scan-Matching Map: Intel Research Lab Seattle Loop Closure
Rao-Blackwellized Mapping with Scan-Matching Map: Intel Research Lab Seattle
Example (Intel Lab) • 15 particles • four times faster than real-timeP4, 2.8GHz • 5cm resolution during scan matching • 1cm resolution in final map joint work with Giorgio Grisetti
FastSLAM – Simulation • Up to 100,000 landmarks • 100 particles • 103 times fewer parameters than EKF SLAM Blue line = true robot path Red line = estimated robot path Black dashed line = odometry
Victoria Park Results • 4 km traverse • 100 particles • Uses negative evidence to remove spurious landmarks Blue path = odometry Red path = estimated path [End courtesy of Mike Montemerlo]
Frontier Based Exploration [Yamauchi et al. 96], [Thrun 98]
Multi-Robot Mapping Robot A Robot B Robot C