discussion topics
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Discussion topics
• SLAM overview
• Range and Odometry data
• Landmarks
• Data Association
• Localisation Algorithms
• Co-operative SLAM
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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
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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
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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
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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
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Range and Odometry Data
• Ultrasonic scanners– Scan width is a problem– Can be overcome by using Triangulation Based Fusion
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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
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Landmarks and Data Association
• Landmark Extraction– Example – Spike Landmarks
• A simple algorithm looking for large variations in range readings• Good for static environments
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Landmarks and Data Association
• Landmark Extraction– Example – RANSAC (Random Sampling Consensus)
• Tries to identify lines from range scans• Good for dynamic indoor environments
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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
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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
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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)
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