cse-473 mobile robot mapping. mapping with raw odometry
TRANSCRIPT
CSE-473
Mobile Robot Mapping
Mapping with Raw Odometry
Why is SLAM a hard problem?
SLAM: robot path and map are both unknown
Robot path error correlates errors in the map
Why is SLAM a hard problem?
• In the real world, the mapping between observations and landmarks is unknown
• Picking wrong data associations can have catastrophic consequences
• Pose error correlates data associations
Robot poseuncertainty
Graphical Model of Mapping
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• Map with N landmarks:(3+2N)-dimensional Gaussian
• Can handle hundreds of dimensions
(E)KF-SLAM
EKF-SLAM
Map Correlation matrix
EKF-SLAM
Map Correlation matrix
EKF-SLAM
Map Correlation matrix
Victoria Park Data Set
[courtesy of E. Nebot]
Victoria Park Data Set Vehicle
[courtesy of E. Nebot]
Data Acquisition
[courtesy of E. Nebot]
Estimated Trajectory
[courtesy of E. Nebot]
Graph-SLAM Idea
Mapping the Allen Center
Comparison to “Ground Truth Comparison to “Ground Truth Map”Map”
Three Mapping RunsThree Mapping Runs
Three Overlayed MapsThree Overlayed Maps
3D Outdoor Mapping
108 features, 105 poses, only few secs using cg.
Map Before Optimization
Map After Optimization
Autonomous Navigation
Courtesy of W. Burgard
Rao-Blackwellized Mapping
),|,( 1:0:1:1 ttt uzmxp
),|(),,|( 1:0:1:11:0:1:1 tttttt uzxpuzxmp
Compute a posterior over the map and possible trajectories of the robot :
robot motionmap trajectory
map and trajectory
measurements
FastSLAM
Robot Pose 2 x 2 Kalman Filters
Landmark 1 Landmark 2 Landmark N…x, y,
Landmark 1 Landmark 2 Landmark N…x, y, Particle#1
Landmark 1 Landmark 2 Landmark N…x, y, Particle#2
Landmark 1 Landmark 2 Landmark N…x, y, Particle#3
ParticleM
…
[Begin courtesy of Mike Montemerlo]
Example
map of particle 1 map of particle 3
map of particle 2
3 particles
Rao-Blackwellized Mapping with Scan-Matching
Map:
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Frontier Based Exploration
[Yamauchi et al. 96], [Thrun 98]