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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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4.2. Dynamic Probabilistic Grids – Bayesian Occupancy Filter concept
LaugierChristianThis video will show how to describe 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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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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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
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1.4. Decisional and Control Architecture for Autonomous Mobile Robots and IV
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5.9. Other approaches: Planning-based approaches
Vasquez GoveaAlejandro DizanIn this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.
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5.4. Bayesian filter inference
Vasquez GoveaAlejandro DizanIn this video we will review the base filter and we will study a particular instance of the Bayesian filter called Hidden Markov models which is a discrete version of a Bayesian filter.
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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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5.6. Predicting Human Motion
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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.
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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.
