Outline Lightning Summary Black Box Model of SIFT SLAM Vision System Challenges in Computer Vision What these challenges mean for visual SLAM How SIFT extracts candidate landmarks How landmarks are tracked in SIFT SLAM Alternative vision-based SLAM systems Open questions
一、现实生活中,我们经常需要获取物体运动的速度,比如:汽车是否超速?摩托车的速度表,控制系统中电机的转速等等。 二、实际测量中线位移的测量比较麻烦(测量装置尺寸很大),一般转换为角位移的测量。同样,线速度的测量一般转换为角速度的测量。这种转换很容易,比如使用一个从动轮即可。 三、思考:如何获得机器人当前运行的速度 Industry Training Center
Partially Observable Markov Decision Processes Part II Additional reading: Anthony R. Cassandra. Exact and Approximate Algorithms for Partially Observable Markov Decision Processes. Ph. D. Thesis. Brown University Department of Computer Science, Providence, RI
Defining problem and model so| ution: Minimizing localization error Comb imize gain in explored map bined Information Utilities Integrated Adaptive Information-based Exploration Algorithm
Vision-based SLAM Mobile Robot Localization And Mapping With Uncertainty using Scale-Invariant Visual Landmarks -e,lowe, Little Vikash Mansinghka Spren Riisgaard Outline
Outline Model-based programming The need for model-based reactive planning The Burton model-based reactive planner Artificial Intelligence Space Systems
Massachusetts Institute of Technology 16.412/6.834 Cognitive Robotics Distributed: Monday, 3/31/04 Objective The purpose of the following handout is to walk you step by step through the execution of the FF planning algorithm, on a simple example. The FF algorithm is presented in the paper: