(Bob) Wang 1999-2004. All right reserved.
Localization, Mapping and Moving Object Tracking
mapping and moving object tracking serve as the basis for
scene understanding, which is a key prerequisite for making a robot
truly autonomous. Simultaneous localization, mapping and moving object
tracking (SLAMMOT) involves not only simultaneous localization and
mapping (SLAM) in dynamic environments but also detecting and tracking
these dynamic objects. It is
believed by many that a solution to the SLAM problem would open up a
vast range of potential applications for autonomous robots. Accordingly, a
solution to the SLAMMOT problem would expand robotic applications in
proximity to human beings where robots work not only for people but
also with people.
establishes a new discipline at the intersection of SLAM and moving
object tracking. Its contributions are two-fold: theoretical and practical.
From a theoretical
perspective, we establish a mathematical framework to integrate SLAM
and moving object tracking, which provides a solid basis for understanding
and solving the whole problem. We describe two solutions: SLAM with
generic objects (GO), and SLAM
with detection and tracking of moving objects (DATMO). SLAM with GO
calculates a joint posterior over all generic objects and the robot. Such an
approach is similar to existing SLAM algorithms, but with additional
structure to allow for motion
modelling of the generic objects. Unfortunately, it is computationally
demanding and infeasible. Consequently, we provide the second solution,
SLAM with DATMO, in which the estimation problem is decomposed into two
separate estimators. By maintaining separate posteriors for the
stationary objects and the moving objects, the resulting estimation
problems are much lower dimensional than SLAM with GO.
From a practical
perspective, we develop algorithms for dealing with the implementation
issues on perception modelling, motion modelling and data association. Regarding
perception modelling, a hierarchical object based representation is
presented to integrate existing feature-based, grid-based and direct
methods. The sampling- and correlation-based range image matching
algorithm is developed to tackle the problems arising from uncertain,
sparse and featureless measurements. With regard to motion modelling,
we describe a move-stop hypothesis tracking algorithm to tackle the
difficulties of tracking ground moving objects. Kinematic information
from motion modelling as well as geometric information from perception
modelling is used to aid data association at different levels. By
following the theoretical guidelines and implementing the described
algorithms, we are able to demonstrate the feasibility of SLAMMOT using
data collected from the Navlab8 and Navlab11 vehicles at high speeds in
crowded urban environments.
| The full paper is available in PDF.
AUTHOR = "Chieh-Chih Wang",
TITLE = "Simultaneous
Localization, Mapping and Moving Object Tracking",
SCHOOL = "Robotics
Institute, Carnegie Mellon University",
YEAR = "2004",
address = "Pittsburgh, PA",
month = "April",
Last Updated: Apr. 23, 2004.