Statistical robotics
applies well-known techniques of statistics and probability
theory (previously already widely used in **computer vision**)
to problems of robotics.
Typical methods include Kalman filters, EM, Bayesian networks,
particle filters, etc.
The robot's belief about its current state is
a probability density function on the possible
states; the belief is continually updated based
on new sensory inputs and a prior probabilistic
model of the effects of actions.
For example,
robot car pioneer Ernst Dickmanns
(1980s and 90s) used Kalman filters to deal with uncertain sensor readings
of his autonomous vehicles.

Since 1990 or so, much of the work in the area of "probabilistic
robotics" has focused
on robot localization and map building,
triggered by the pioneering work of
Durrant-Whyte's group
(Kalman filters / simultaneous localization and map building SLAM)
as well as Smith *et al.*
Here is a list of original or frequently
cited papers of the 1990s, as well as a more recent works:

**1.**
R. Smith, M. Self, and P. Cheeseman.
Estimating uncertain spatial relationships in robotics.
In I.J. Cox and G.T. Wilfong, editors, * Autonomous Robot
Vehicles,* volume 8, 167-193, 1990.

**2.**
J. Leonard and H. Durrant-Whyte. Mobile Robot Localization by
Tracking Geometric Beacons. *IEEE Trans. Robotics and Automation *
7(3), 1991.

**3.**
J. J. Leonard and H. Durrant-Whyte.
Simultaneous map building and localization for an autonomous mobile
robot. * IEEE International Conference on Intelligent Robot Systems,
Osaka, Japan,* 1991.

**4.**
W. Burgard, D. Fox, D. Hennig, and T. Schmidt.
Estimating the absolute position of a mobile robot using position
probability grids. * AAAI/IAAI,* Vol. 2, 896-901, 1996.

**5.**
G. Dissanayake, P. Newman, S. Clark, H. Durrant-Whyte, and M. Csorba.
A solution to the simultaneous localization and map building (SLAM) problem.
*IEEE Transactions on Robotics and Automation,* 17(3):229-241, 2001.

**6.**
M. Beetz, T. Schmitt, R. Hanek, S. Buck, F. Stulp, D. Schroeter, and B. Radig.
The AGILO robot soccer team: experience-based learning and
probabilistic reasoning in autonomous robot control.
* Autonomous Robots,* 2004.