12 research outputs found
Fast 3D Extended Target Tracking using NURBS Surfaces
This paper proposes fast and novel methods to jointly estimate the target's
unknown 3D shape and dynamics. Measurements are noisy and sparsely distributed
3D points from a light detection and ranging (LiDAR) sensor. The methods
utilize non-uniform rational B-splines (NURBS) surfaces to approximate the
target's shape. One method estimates Cartesian scaling parameters of a NURBS
surface, whereas the second method estimates the corresponding NURBS weights,
too. Major advantages are the capability of estimating a fully 3D shape as well
as the fast processing time. Real-world evaluations with a static and dynamic
vehicle show promising results compared to state-of-the-art 3D extended target
tracking algorithms.Comment: In Proceedings of IEEE Intelligent Transportation Systems Conference
(ITSC), 201
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes
In this study, we investigate the problem of tracking objects with unknown
shapes using three-dimensional (3D) point cloud data. We propose a Gaussian
process-based model to jointly estimate object kinematics, including position,
orientation and velocities, together with the shape of the object for online
and offline applications. We describe the unknown shape by a radial function in
3D, and induce a correlation structure via a Gaussian process. Furthermore, we
propose an efficient algorithm to reduce the computational complexity of
working with 3D data. This is accomplished by casting the tracking problem into
projection planes which are attached to the object's local frame. The resulting
algorithms can process 3D point cloud data and accomplish tracking of a dynamic
object. Furthermore, they provide analytical expressions for the representation
of the object shape in 3D, together with confidence intervals. The confidence
intervals, which quantify the uncertainty in the shape estimate, can later be
used for solving the gating and association problems inherent in object
tracking. The performance of the methods is demonstrated both on simulated and
real data. The results are compared with an existing random matrix model, which
is commonly used for extended object tracking in the literature
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Directional Estimation for Robotic Beating Heart Surgery
In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart
Simultaneous Tracking and Shape Estimation of Extended Objects
This work is concerned with the simultaneous tracking and shape estimation of a mobile extended object based on noisy sensor measurements. Novel methods are developed for coping with the following two main challenges: i) The computational complexity due to the nonlinearity and high-dimensionality of the problem, and ii) the lack of statistical knowledge about possible measurement sources on the extended object
Tracking Extended Objects with Active Models and Negative Measurements
Extended object tracking deals with estimating the shape and pose of an object based on noisy point measurements. This task is not straightforward, as we may be faced with scarce low-quality measurements, little a priori information, or we may be unable to observe the entire target. This work aims to address these challenges by incorporating ideas from active contours and exploiting information from negative measurements, which tell us where the target cannot be
Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
We discuss theory and application of extended object tracking. This task is challenging as sensor noise prevents a correct association of the measurements to their sources on the object, the shape itself might be unknown a priori, and due to occlusion effects, only parts of the object are visible at a given time. We propose an approach to track the parameters of arbitrary objects, which provides new solutions to the above challenges, and marks a significant advance to the state of the art