Discussion topics

• SLAM overview

• Range and Odometry data

• Landmarks

• Data Association

• Localisation Algorithms

• Co-operative SLAM

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

SLAM overview

• The basic process1. Move2. Take range and odometry data3. Update state with odometry data4. Update state with previously seen landmarks5. Update state with new landmarks6. Repeat

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

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

Range and Odometry Data

• Ultrasonic scanners– Scan width is a problem– Can be overcome by using Triangulation Based Fusion

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

Landmarks and Data Association

• Landmark Extraction– Example – Spike Landmarks

• A simple algorithm looking for large variations in range readings• Good for static environments

Landmarks and Data Association

• Landmark Extraction– Example – RANSAC (Random Sampling Consensus)

• Tries to identify lines from range scans• Good for dynamic indoor environments

Landmarks and Data Association

• 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

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

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)