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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

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

    2. 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

    3. 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

    4. 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

    5. 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

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

    7. 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

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

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

    10. 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

    11. 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

    12. 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)