2,504 research outputs found
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
Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
In a typical multitarget tracking (MTT) scenario, the sensor state is either
assumed known, or tracking is performed in the sensor's (relative) coordinate
frame. This assumption does not hold when the sensor, e.g., an automotive
radar, is mounted on a vehicle, and the target state should be represented in a
global (absolute) coordinate frame. Then it is important to consider the
uncertain location of the vehicle on which the sensor is mounted for MTT. In
this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT
filter, which jointly tracks the uncertain vehicle state and target states.
Measurements collected by different sensors mounted on multiple vehicles with
varying location uncertainty are incorporated sequentially based on the arrival
of new sensor measurements. In doing so, targets observed from a sensor mounted
on a well-localized vehicle reduce the state uncertainty of other poorly
localized vehicles, provided that a common non-empty subset of targets is
observed. A low complexity filter is obtained by approximations of the joint
sensor-feature state density minimizing the Kullback-Leibler divergence (KLD).
Results from synthetic as well as experimental measurement data, collected in a
vehicle driving scenario, demonstrate the performance benefits of joint
vehicle-target state tracking.Comment: 13 pages, 7 figure
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
Understanding ego-motion and surrounding vehicle state is essential to enable
automated driving and advanced driving assistance technologies. Typical
approaches to solve this problem use fusion of multiple sensors such as LiDAR,
camera, and radar to recognize surrounding vehicle state, including position,
velocity, and orientation. Such sensing modalities are overly complex and
costly for production of personal use vehicles. In this paper, we propose a
novel machine learning method to estimate ego-motion and surrounding vehicle
state using a single monocular camera. Our approach is based on a combination
of three deep neural networks to estimate the 3D vehicle bounding box, depth,
and optical flow from a sequence of images. The main contribution of this paper
is a new framework and algorithm that integrates these three networks in order
to estimate the ego-motion and surrounding vehicle state. To realize more
accurate 3D position estimation, we address ground plane correction in
real-time. The efficacy of the proposed method is demonstrated through
experimental evaluations that compare our results to ground truth data
available from other sensors including Can-Bus and LiDAR
Variational Bayesian Expectation Maximization for Radar Map Estimation
For self-localization, a detailed and reliable map of the environment can be used to relate sensor data to static features with known locations. This paper presents a method for construction of detailed radar maps that describe the expected intensity of detections. Specifically, the measurements are modelled by an inhomogeneous Poisson process with a spatial intensity function given by the sum of a constant clutter level and an unnormalized Gaussian mixture. A substantial difficulty with radar mapping is the presence of data association uncertainties, i.e., the unknown associations between measurements and landmarks. In this paper, the association variables are introduced as hidden variables in a variational Bayesian expectation maximization (VBEM) framework, resulting in a computationally efficient mapping algorithm that enables a joint estimation of the number of landmarks and their parameters
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior
for multiple extended object filtering. A Poisson point process is used to
describe the existence of yet undetected targets, while a multi-Bernoulli
mixture describes the distribution of the targets that have been detected. The
prediction and update equations are presented for the standard transition
density and measurement likelihood. Both the prediction and the update preserve
the PMBM form of the density, and in this sense the PMBM density is a conjugate
prior. However, the unknown data associations lead to an intractably large
number of terms in the PMBM density, and approximations are necessary for
tractability. A gamma Gaussian inverse Wishart implementation is presented,
along with methods to handle the data association problem. A simulation study
shows that the extended target PMBM filter performs well in comparison to the
extended target d-GLMB and LMB filters. An experiment with Lidar data
illustrates the benefit of tracking both detected and undetected targets
Variational Bayesian EM for SLAM
Designing accurate, robust and cost-effective systems is an important aspect of the research on self-driving vehicles. Radar is a common part of many existing automotive solutions and it is robust to adverse weather and lighting conditions, as such it can play an important role in the design of a self-driving vehicle. In this paper, a radar-based simultaneous localization and mapping (SLAM) algorithm using variational Bayesian expectation maximization (VBEM) is presented. The VBEM translates the inference problem to an optimization one. It provides an efficient and powerful method to estimate the unknown data association variables as well as the map of the environment as perceived by a radar and the unknown trajectory of the vehicle
Overview of Environment Perception for Intelligent Vehicles
This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The
state-of-the-art algorithms and modeling methods for intelligent
vehicles are given, with a summary of their pros and cons. A
special attention is paid to methods for lane and road detection,
traffic sign recognition, vehicle tracking, behavior analysis, and
scene understanding. In addition, we provide information about
datasets, common performance analysis, and perspectives on
future research directions in this area
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