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

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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

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[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

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Frontier Based Exploration

[Yamauchi et al. 96], [Thrun 98]