Notice
4.2. Dynamic Probabilistic Grids – Bayesian Occupancy Filter concept
- document 1 document 2 document 3
- niveau 1 niveau 2 niveau 3
Descriptif
This video will showhow to describe Bayesian occupancy filter concept.
Thème
Documentation
Liens
Avec les mêmes intervenants et intervenantes
-
4.1. Robot Perception for Dynamic environments: Outline and DP-Grids concept
LAUGIER Christian
The 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
-
4.6. Detection and Tracking of Mobile Objects – Model and Grid based approaches
LAUGIER Christian
This video addresses the question of model-based and grid-based approaches.
-
4.8. Situation Awareness – Problem statement and Motion / Prediction Models
LAUGIER Christian
This videos addresses the problem of situation awareness and motion prediction models.
-
4.4. Object level Perception functions (SLAM + DATMO)
LAUGIER Christian
This video is dedicated to the object level perception functions: Simultaneous Localization Mapping (SLAM) and Detection and Tracking surrounding Mobile Objects (DATMO).
-
4.7. Embedded Bayesian Perception and Short-term collision risk (DP-Grid level)
LAUGIER Christian
This video deals with embedded Bayesian perception and short-term collision risk.
-
4.3. Dynamic Probabilistic Grids – Implementation approaches
LAUGIER Christian
This video addresses the problem of the practical implementation for the dynamic probabilistic grid.
-
4.5. Detection and Tracking of Mobile Objects – Problem and Approaches
LAUGIER Christian
This video adresses the Detection and Tracking of Mobile Objects (DATMO) problem.
-
4.9. Situation Awareness – Collision Risk Assessment and Decision (Object level)
LAUGIER Christian
This video addresses the problem of collision risk assessment and decision.
-
1.4. Decisional and Control Architecture for Autonomous Mobile Robots and IV
LAUGIER Christian
This video is presenting the decision and control architecture for autonomous mobile robots and intelligent vehicles. The question is how to control robot action in a dynamic world populated by
-
1.6. Sensing technologies: Robot Control and HRI
LAUGIER Christian
This video addresses sensing technology, and focuses on robot control and human-robot interaction applications.
-
1.1. Socio-economic context
LAUGIER Christian
This is the first lesson of the course on mobile robots and autonomous vehicles. It addresses the objectives, the challenges, the state of the art and the technologies.
-
1.3. New challenges for Robotics in Human Environments
LAUGIER Christian
Introducing robots in human environments bring new challenges to robotics.
Sur le même thème
-
Et si l’intelligence artificielle déferlait sur les océans ?
L’évolution des technologies d’observation et de modélisation a joué un rôle central dans l’accroissement des connaissances sur le fonctionnement des océans ou dans le développement des activités
-
La vidéo sous-marine au service de la recherche halieutique
Accessible à de nombreuses applications, tant en biologie ou qu'en technologie des pêches, la vidéo sous-marine est de plus en plus utilisée dans le domaine de la recherche halieutique. Les progrès
-
5.5. From trajectories to discrete time-state models
VASQUEZ GOVEA Alejandro Dizan
In this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.
-
5.8. Other approaches: Social Forces
VASQUEZ GOVEA Alejandro Dizan
In this video we will review one of the alternatives we are proposing to the use of Hidden Markov models and typical trajectories: the Social Force model.
-
5.6. Predicting Human Motion
VASQUEZ GOVEA Alejandro Dizan
In video 5.5 we have defined an HMM in Python. In this video we are going to learn how to use it to estimate and predict motion.
-
5.9. Other approaches: Planning-based approaches
VASQUEZ GOVEA Alejandro Dizan
In this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.
-
5.4. Bayesian filter inference
VASQUEZ GOVEA Alejandro Dizan
In 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.
-
5.7. Typical Trajectories: drawbacks
VASQUEZ GOVEA Alejandro Dizan
In previous videos we have discussed how to implement the typical trajectories and motion patterns approach. In this video we are going to discuss what are the drawbacks of such an approach,
-
4.4. Object level Perception functions (SLAM + DATMO)
LAUGIER Christian
This video is dedicated to the object level perception functions: Simultaneous Localization Mapping (SLAM) and Detection and Tracking surrounding Mobile Objects (DATMO).
-
5.3a. Learning typical trajectories 1/2
VASQUEZ GOVEA Alejandro Dizan
In video 5.2 we showed how to apply the expectation maximization clustering algorithm to two-dimensional data. In this video we will learn how to apply it to trajectory data. And then we will be
-
4.7. Embedded Bayesian Perception and Short-term collision risk (DP-Grid level)
LAUGIER Christian
This video deals with embedded Bayesian perception and short-term collision risk.
-
4.3. Dynamic Probabilistic Grids – Implementation approaches
LAUGIER Christian
This video addresses the problem of the practical implementation for the dynamic probabilistic grid.
