15 research outputs found
Leader-assisted localization approach for a heterogeneous multi-robot system
This thesis presents the design, implementation, and validation of a novel leader assisted localization framework for a heterogeneous multi-robot system (MRS) with sensing and communication range constraints. It is assumed that the given heterogeneous MRS has a more powerful robot (or group of robots) with accurate self localization capabilities (leader robots) while the rest of the team (child robots), i.e. less powerful robots, is localized with the assistance of leader robots and inter-robot observation between teammates. This will eventually pose a condition that the child
robots should be operated within the sensing and communication range of leader
robots. The bounded navigation space therefore may require added algorithms to
avoid inter-robot collisions and limit robotsâ maneuverability. To address this limitation,
first, the thesis introduces a novel distributed graph search and global pose composition
algorithm to virtually enhance the leader robotsâ sensing and communication
range while avoiding possible double counting of common information. This allows
child robots to navigate beyond the sensing and communication range of the leader
robot, yet receive localization services from the leader robots. A time-delayed measurement
update algorithm and a memory optimization approach are then integrated
into the proposed localization framework. This eventually improves the robustness
of the algorithm against the unknown processing and communication time-delays associated
with the inter-robot data exchange network. Finally, a novel hierarchical sensor fusion architecture is introduced so that the proposed localization scheme can
be implemented using inter-robot relative range and bearing measurements.
The performance of the proposed localization framework is evaluated through a series
of indoor experiments, a publicly available multi-robot localization and mapping
data-set and a set of numerical simulations. The results illustrate that the proposed
leader-assisted localization framework is capable of establishing accurate and nonoverconfident
localization for the child robots even when the child robots operate
beyond the sensing and communication boundaries of the leader robots
A review of laser scanning for geological and geotechnical applications in underground mining
Laser scanning can provide timely assessments of mine sites despite adverse
challenges in the operational environment. Although there are several published
articles on laser scanning, there is a need to review them in the context of
underground mining applications. To this end, a holistic review of laser
scanning is presented including progress in 3D scanning systems, data
capture/processing techniques and primary applications in underground mines.
Laser scanning technology has advanced significantly in terms of mobility and
mapping, but there are constraints in coherent and consistent data collection
at certain mines due to feature deficiency, dynamics, and environmental
influences such as dust and water. Studies suggest that laser scanning has
matured over the years for change detection, clearance measurements and
structure mapping applications. However, there is scope for improvements in
lithology identification, surface parameter measurements, logistic tracking and
autonomous navigation. Laser scanning has the potential to provide real-time
solutions but the lack of infrastructure in underground mines for data
transfer, geodetic networking and processing capacity remain limiting factors.
Nevertheless, laser scanners are becoming an integral part of mine automation
thanks to their affordability, accuracy and mobility, which should support
their widespread usage in years to come
Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone
This paper proposes a multi-object tracking (MOT) algorithm for traffic
monitoring using a drone equipped with optical and thermal cameras. Object
detections on the images are obtained using a neural network for each type of
camera. The cameras are modelled as direction-of-arrival (DOA) sensors. Each
DOA detection follows a von-Mises Fisher distribution, whose mean direction is
obtain by projecting a vehicle position on the ground to the camera. We then
use the trajectory Poisson multi-Bernoulli mixture filter (TPMBM), which is a
Bayesian MOT algorithm, to optimally estimate the set of vehicle trajectories.
We have also developed a parameter estimation algorithm for the measurement
model. We have tested the accuracy of the resulting TPMBM filter in synthetic
and experimental data sets
Predictive parameter estimation for Bayesian filtering
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 113-117).In this thesis, I develop CELLO, an algorithm for predicting the covariances of any Gaussian model used to account for uncertainty in a complex system. The primary motivation for this work is state estimation; often, complex raw sensor measurements are processed into low dimensional observations of a vehicle state. I argue that the covariance of these observations can be well-modelled as a function of the raw sensor measurement, and provide a method to learn this function from data. This method is computationally cheap, asymptotically correct, easy to extend to new sensors, and noninvasive, in the sense that it augments, rather than disrupts, existing filtering algorithms. I additionally present two important variants; first, I extend CELLO to learn even when ground truth vehicle states are unavailable; and second, I present an equivalent Bayesian algorithm. I then use CELLO to learn covariance models for several systems, including a laser scan-matcher, an optical flow system, and a visual odometry system. I show that filtering using covariances predicted by CELLO can quantitatively improve estimator accuracy and consistency, both relative to a fixed covariance model and relative to carefully tuned domain-specific covariance models.by William Vega-Brown.S.M