Centre for Autonomous Systems
Open Problems in SLAM?
© Henrik I Christensen
Centre for Autonomous Systems
What are good thesis topics?
Problems of adequate quality to warrant
three – fours man years of study?
Focussed problem
Timely – can be studied now/soon
New problems?
© Henrik I Christensen
1
Centre for Autonomous Systems
Group work
Groups of 5 people
Define a “good problem”?
Motivate why this is a good problem?
Speaker to present the problem
1-3 minutes presentation
© Henrik I Christensen
Centre for Autonomous Systems
Topics 1
3C (consistency, convergence &
complexity) + optimality + reliability
Benchmark problems
Knowing when to use which method?
Comparative studies of multiple methods
© Henrik I Christensen
2
Centre for Autonomous Systems
Topics 2
Representation
What is in common between environments
What is a good feature/recognition …
Multi-hypotheses methods
Map models and position estimates
Not committing to a decision until you have to!
Tessellation of space conceptually!
© Henrik I Christensen
Centre for Autonomous Systems
Topics 3
Representation
How much accuracy is needed for a task
Abstract
Hybrid
Topological maps
Fusion of hybrid
reps
Geometric
High accuracy/fidelity
© Henrik I Christensen
3
Centre for Autonomous Systems
Topics 4
SLAM with high level features
Recognition of objects (say doors)
Object recognition
Probabilistic framework
© Henrik I Christensen
Centre for Autonomous Systems
Topics 5
Error modeling between sensors
Ex GPS vs. INS (estimate vs truth)
Representation(s)
Intelligent use of data to sensible extract
features (or surfaces)
© Henrik I Christensen
4
Centre for Autonomous Systems
Topics 6
Exploration in SLAM space
Planning and information gain to define
exploration strategy
Analytic model of SLAM to define a strategy!
Assume landmarks (ephemeral)
Use of multiple vehicles
Handling of sensory failures
© Henrik I Christensen
Centre for Autonomous Systems
Topic 7
What are the maps needed for robot tasks
Multi-modal or complex maps
3D, Vision, …
© Henrik I Christensen
5
Centre for Autonomous Systems
Topics 8
Difficult SLAM
With crappy sensor
In dynamic environments
Using difficult to recognize features
© Henrik I Christensen
Centre for Autonomous Systems
Topic 9
SLAM without landmark
What will it do to the error models
What would you need to “recognize”
What would be the time complexity
“temporal identification” of features
(moving landmarks a la G. Dudek)
© Henrik I Christensen
6
Centre for Autonomous Systems
Topic 10
SLAM issues
Algorithms
Data association
Feature robustness
Could learning be used for feature
“improvement”
New features vs Tuning of Detectors
Local vs Global issues
© Henrik I Christensen
Centre for Autonomous Systems
Topics 11
Siegwart, Burgard, Dias, Chatila
Reduction in complexity (prototypical)
Bayes ! markov ! Particle ! Kalman
Real outdoor 3D SLAM
Indoor & outdoor SLAM w. vision
Multi-sensory / multi-modal fusion
Complex representations
Semantics: interp simplifies identf & data assoc.
Fundamental approach to closing the loop
Dyn envs (intg/filt dyn objs)
© Henrik I Christensen
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