Probabilistic Algorithms for
Mobile Robot Mapping

"Based on the paper"

Slide 3

Museum Tour-Guide Robots

The Nursebot Initiative

Slide 6

Mapping: The Problem

Mapping: The Problem

Milestone Approaches

3D Mapping

Take-Home Message

Slide 12

Bayes Filters

Bayes Filters in Localization

Bayes Filters for Mapping

Kalman Filters (SLAM)

Underwater Mapping with SLAM
Courtesy of Hugh Durrant-Whyte, Univ of Sydney

Large-Scale SLAM Mapping
Courtesy of John Leonard, MIT

SLAM: Limitations

Slide 20

Unknown Data Association: EM

CMU’s Wean Hall (80 x 25 meters)

EM Mapping, Example (width 45 m)

EM Mapping: Limitations

Slide 25

The Goal

Real-Time Approximation (ICRA paper)

Incremental ML: Not A Good Idea

Real-Time Approximation

Real-Time Approximation

Importance of Posterior Pose Estimate

Online Mapping with Posterior
Courtesy of Kurt Konolige, SRI, DARPA-TMR

Accuracy: “The Tech” Museum, San Jose

Multi-Robot Mapping

Multi-Robot Exploration

3D Volumetric Mapping

3D Structure Mapping

3D Texture Mapping

Fine-Grained Structure:
Can We Do Better?

Slide 40

Multi-Planar 3D Mapping

3D Multi-Plane Mapping Problem

Expected Log-Likelihood Function

EM To The Rescue!

Results

The Obvious Next Step

Underwater Mapping    (with University of Sydney)

Slide 48

Take-Home Message

Open Problems

Open Problems, con’t

Slide 52