Notice
4.4. Object level Perception functions (SLAM + DATMO)
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Descriptif
This video is dedicated tothe object level perception functions: Simultaneous Localization Mapping (SLAM) and Detection and Tracking surrounding Mobile Objects (DATMO).
Avec les mêmes intervenants et intervenantes
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4.7. Embedded Bayesian Perception and Short-term collision risk (DP-Grid level)
LaugierChristianThis video deals with embedded Bayesian perception and short-term collision risk.
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4.2. Dynamic Probabilistic Grids – Bayesian Occupancy Filter concept
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4.5. Detection and Tracking of Mobile Objects – Problem and Approaches
LaugierChristianThis video adresses the Detection and Tracking of Mobile Objects (DATMO) problem.
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4.9. Situation Awareness – Collision Risk Assessment and Decision (Object level)
LaugierChristianThis video addresses the problem of collision risk assessment and decision.
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4.3. Dynamic Probabilistic Grids – Implementation approaches
LaugierChristianThis video addresses the problem of the practical implementation for the dynamic probabilistic grid.
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4.6. Detection and Tracking of Mobile Objects – Model and Grid based approaches
LaugierChristianThis video addresses the question of model-based and grid-based approaches.
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4.8. Situation Awareness – Problem statement and Motion / Prediction Models
LaugierChristianThis videos addresses the problem of situation awareness and motion prediction models.
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4.1. Robot Perception for Dynamic environments: Outline and DP-Grids concept
LaugierChristianThe fourth part of the course addresses perception, situation awareness and decision making. In this first video, we're giving an outline of the problem and introducing the new concept of dynamic
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1.7. Basic technologies for Navigation in Dynamic Human Environments
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1.2. Technological evolution of Robotics and State of the Art
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1.5. Sensing technologies: Object Detection
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1.8. Intelligent Vehicles: Context and State of the Art
LaugierChristianThis video introduces intelligent vehicles and presents more specifically the context and a state of the art.
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5.6. Predicting Human Motion
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5.9. Other approaches: Planning-based approaches
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5.4. Bayesian filter inference
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5.7. Typical Trajectories: drawbacks
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5.5. From trajectories to discrete time-state models
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5.8. Other approaches: Social Forces
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4.9. Situation Awareness – Collision Risk Assessment and Decision (Object level)
LaugierChristianThis video addresses the problem of collision risk assessment and decision.
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4.3. Dynamic Probabilistic Grids – Implementation approaches
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5.2. Expectation-Maximization Clustering
Vasquez GoveaAlejandro DizanIn this video, we are going to start working towards building a motion model.
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4.6. Detection and Tracking of Mobile Objects – Model and Grid based approaches
LaugierChristianThis video addresses the question of model-based and grid-based approaches.
