2. Discussion topics SLAM overview Range and Odometry data Landmarks
Data Association
Localisation Algorithms
Co-operative SLAM

3. SLAM overview The general Idea Simultaneous Localisation and Mapping
Large base of research on the topic
Starting with no priori, build a geometric map of the environment

4. SLAM overview The basic process Move Take range and odometry data
Update state with odometry data
Update state with previously seen landmarks
Update state with new landmarks
Repeat

5. Range and Odometry Data
2 main inputs to a SLAM algorithm used to update the state
Odometry data is used to get an estimated position of the robot
Range and bearings are nearby landmarks are taken
These are passed through the localisation algorithm

6. Range and Odometry Data
3 common types of scanners. Each with their own problems
Laser Scanners
Almost perfect, but Expensive!
Video cameras
Extremely complex algorithms required
Highly dependent on lighting conditions
Ultrasonic scanners
Scan width
Multiple reflections and crosstalk

7. Range and Odometry Data
Ultrasonic scanners
Scan width is a problem
Can be overcome by using Triangulation Based Fusion

8. Landmarks and Data Association
Landmarks are used to correct the estimation of the robot’s position given by odometry data
Algorithm implementation is dependent on the type of landmark expected
Static vs Dynamic environment

9. Landmarks and Data Association
Landmark Extraction
Example – Spike Landmarks
A simple algorithm looking for large variations in range readings
Good for static environments

10. Landmarks and Data Association
Landmark Extraction
Example – RANSAC (Random Sampling Consensus)
Tries to identify lines from range scans
Good for dynamic indoor environments

11. Landmarks and Data Association
Proper association of landmarks from previous scans is paramount to the success of the algorithm
Allows the algorithm to correct its perceived position
Makes ‘loop closure’ a possibility
Difficulties
It may be easy for humans, but not programmatically
Odometry and sensor error

12. Localisation algorithms
2 of the most popular algorithms
The Extended Kalman Filter
Uses a Kalman filter that is extended to use range data to help correct the position
Monte Carlo Localisation
Based on Particle Filters
Creates a set of random poses (states)
Filters out the most unlikely poses recursively

13. Co-operative SLAM A very new aspect of research in the area of SLAM
Various implementations have been tested
Simply using a common state and landmark vector
A master slave configuration (confirmation of readings)