'Probabilistic robotics' presentation slideshows

Nelson Series Talk

764 views • 76 slides

CS 547: Sensing and Planning in Robotics

CS 547: Sensing and Planning in Robotics. Gaurav S. Sukhatme Computer Science Robotics Research Laboratory University of Southern California gaurav@usc.edu http://robotics.usc.edu/~gaurav. Administrative Matters. Signup - please fill in the details on one of the sheets

582 views • 46 slides

CS 547: Sensing and Planning in Robotics

CS 547: Sensing and Planning in Robotics. Gaurav S. Sukhatme Computer Science Robotics Research Laboratory University of Southern California gaurav@usc.edu http://robotics.usc.edu/~gaurav. Administrative Matters. Signup - please fill in the details on one of the sheets

594 views • 46 slides

SLAM: Simultaneous Localization and Mapping: Part I

2.26k views • 34 slides

Probabilistic Robotics: A Tutorial

518 views • 31 slides

Probabilistic Robotics

475 views • 28 slides

CHAPTER 14

CHAPTER 14. ROBOTICS. Mobile Robots. Manipulator Robots. Sensors and Effectors. Motion as Search. Decomposition Methods. Approximate Decomposition. Skeletonization Methods. Probabilistic Roadmaps. Potential Field Methods. So far: implicit preference for short paths

339 views • 20 slides

Probabilistic Robotics

Probabilistic Robotics. Planning and Control: Markov Decision Processes. Problem Classes. Deterministic vs. stochastic actions Full vs. partial observability. Deterministic, fully observable. Stochastic, Fully Observable. Stochastic, Partially Observable. Markov Decision Process (MDP).

239 views • 15 slides

Autonomous Mobile Robotics

Autonomous Mobile Robotics. People. Lecturers Daniel Asmar ( da20@aub.edu.lb ) Sevag Babikian ( sb56@ aub.edu.lb ). General Information. Textbooks (references) Kelly, A. Mobile Robotics: Mathematics, Models, and Methods. Cambridge University Press, 2013

362 views • 14 slides

Probabilistic Robotics

Probabilistic Robotics. FastSLAM with known correspondences. Path posterior estimated by a particle filter Feature locations estimated by EKFs. M particles M*N EKFs. Each particle contains: Path estimation EKF for each feature location. Pose estimate 1-4 Update features 5-24

228 views • 13 slides