Dr.Kawewong Ph.D Thesis
Augmented reality
FINAL REPORT-IndoorQuadcopter
Self-Supervised Sensor Learning and Its Application
Tim Oberhauser
Project Tango
MICHAEL MILFORD, DAVID PRASSER, AND GORDON WYETH FOLAMI ALAMUDUN GRADUATE STUDENT COMPUTER SCIENCE & ENGINEERING TEXAS A&M UNIVERSITY RatSLAM on the Edge:
ECGR4161/5196 – July 28, 2011 Read Chapter 5 Exam 2 contents: Labs 0, 1, 2, 3, 4, 6 Homework 1, 2, 3, 4, 5 Book Chapters 1, 2, 3, 4, 5 All class notes.
Mapping with Known Poses Pieter Abbeel UC Berkeley EECS Many slides adapted from Thrun, Burgard and Fox, Probabilistic Robotics TexPoint fonts used in.
Probabilistic Robotics SLAM. 2 Given: The robot’s controls Observations of nearby features Estimate: Map of features Path of the robot The SLAM Problem.
Probabilistic Robotics SLAM. 2 Given: The robot’s controls (U 1:t ) Observations of nearby features (Z 1:t ) Estimate: Map of features (m) Pose / Path.
Parallel Tracking and Mapping for Small AR Workspaces Parallel Tracking and Mapping for Small AR Workspaces Vision Seminar 2008. 9. 4 (Thu) Young Ki Baik.
