Simultaneous Localization and
Mapping (SLAM)
Lecture 01
Introduction
SLAM Objective
? Place a robot in an unknown location in an unknown environment and
have the robot incrementally build a map of this environment while simultaneously using this map to compute vehicle location
? SLAM began with seminal paper by R. Smith, M. Self, and P.
Cheeseman in 1990
? A solution to SLAM has been seen as the “Holy Grail”
– Would enable robots to operate in an environment without a priori knowledge of obstacle locations
? Research over the last decade has shown that a solution is possible!!
The Localization Problem
Defined
? A map m of landmark locations is known a priori
? Take measurements of landmark location zk (i.e. distance and bearing)
? Determine vehicle location xk based on zk
– Need filter if sensor is noisy!
? ? ? ?
xk: location of vehicle at time k uk: a control vector applied at k-1 to drive the vehicle from xk-1 to xk zk: observation of a landmark taken at time k
Xk: history of states {x
1, x2, x3, …, xk} ?
Uk: history of control inputs {u
1, u2,
u3, …, uk}
? m: set of all landmarks
SLAM经典入门教程
