44 research outputs found
Tracking and Estimation Algorithms for Bearings Only Measurements
The Bearings-only tracking problem is to estimate the state of a moving object from noisy
observations of its direction relative to a sensor. The Kalman filter, which provides least
squares estimates for linear Gaussian filtering problems is not directly applicable because
of the highly nonlinear measurement function of the state, representing the bearings
measurements and so other types of filters must be considered. The shifted Rayleigh filter (SRF) is a highly effective moment-matching bearings-only tracking algorithm which has
been shown, in 2D, to achieve the accuracy of computationally demanding particle filters in situations where the well-known extended Kalman filter and unscented Kalman filter often fail.
This thesis has two principal aims. The first is to develop accurate and computationally efficient algorithms for bearings-only tracking in 3D space. We propose algorithms based
on the SRF, that allow tracking, in the presence of clutter, of both nonmaneuvering
and maneuvering targets. Their performances are assessed, in relation to competing
methods, in highly challenging tracking scenarios, where they are shown to match the
accuracy of high-order sophisticated particle filters, at a fraction of the computational cost.
The second is to design accurate and consistent algorithms for bearings-only simultaneous
localization and mapping (SLAM). The difficulty of this problem, originating
from the uncertainty in the position and orientation of the sensor, and the absence of
range information of observed landmarks, motivates the use of advanced bearings-only
tracking algorithms. We propose the quadrature-SRF SLAM algorithm, which is a
moment-matching filter based on the SRF, that numerically evaluates the exact mean
and covariance of the posterior. Simulations illustrate the accuracy and consistency of its
estimates in a situation where a widely used moment-matching algorithm fails to produce
consistent estimates. We also propose a Rao-Blackwellized SRF implementation of a
particle filter, which, however, does not exhibit favorable consistency properties
Multi-Robot FastSLAM for Large Domains
For a robot to build a map of its surrounding area, it must have accurate position information within the area, and to obtain accurate position information within the area, the robot needs to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these maps poses a serious limitation to the algorithm\u27s capability when the problem space is large. The problem will only get worse if the algorithm is adapted to multi-robot mapping. This thesis presents an alternate mapping algorithm that extends the single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations (ASR) to represent the world. But each particle still maintains a local grid to map its vicinity and periodically this grid map is converted into an ASR. An ASR expresses a world in polygons requiring only a minimal amount of memory space. By using this altered mapping strategy, the problem faced in FastSLAM when mapping a large domain can be alleviated. In this algorithm, each robot maps separately, and when two robots encounter each other they exchange range and odometry readings from their last encounter to this encounter. Each robot then sets up another filter for the other robot\u27s data and incrementally updates its own map, incorporating the passed data and its own data at the same time. The passed data is processed in reverse by the receiving robot as if a virtual robot is back-tracking the path of the other robot. The algorithm is demonstrated using three data sets collected using a single robot equipped with odometry and laser-range finder sensors
Exactly Sparse Delayed-State Filters for View-Based SLAM
This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86062/1/reustice-25.pd
Sparse Bayesian information filters for localization and mapping
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008This thesis formulates an estimation framework for Simultaneous Localization and
Mapping (SLAM) that addresses the problem of scalability in large environments.
We describe an estimation-theoretic algorithm that achieves significant gains in computational
efficiency while maintaining consistent estimates for the vehicle pose and
the map of the environment.
We specifically address the feature-based SLAM problem in which the robot represents
the environment as a collection of landmarks. The thesis takes a Bayesian
approach whereby we maintain a joint posterior over the vehicle pose and feature
states, conditioned upon measurement data. We model the distribution as Gaussian
and parametrize the posterior in the canonical form, in terms of the information
(inverse covariance) matrix. When sparse, this representation is amenable to computationally
efficient Bayesian SLAM filtering. However, while a large majority of the
elements within the normalized information matrix are very small in magnitude, it is
fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability
benefits of a sparse parametrization by explicitly pruning these weak links in an effort
to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information
Filter (SEIF), which has laid much of the groundwork concerning the computational
benefits of the sparse canonical form. The thesis performs a detailed analysis of the
process by which the SEIF approximates the sparsity of the information matrix and
reveals key insights into the consequences of different sparsification strategies. We
demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent,
suffering from exaggerated confidence estimates. This overconfidence has
detrimental effects on important aspects of the SLAM process and affects the higher
level goal of producing accurate maps for subsequent localization and path planning.
This thesis proposes an alternative scalable filter that maintains sparsity while
preserving the consistency of the distribution. We leverage insights into the natural
structure of the feature-based canonical parametrization and derive a method that
actively maintains an exactly sparse posterior. Our algorithm exploits the structure
of the parametrization to achieve gains in efficiency, with a computational cost that
scales linearly with the size of the map. Unlike similar techniques that sacrifice
consistency for improved scalability, our algorithm performs inference over a posterior
that is conservative relative to the nominal Gaussian distribution. Consequently, we
preserve the consistency of the pose and map estimates and avoid the effects of an
overconfident posterior.
We demonstrate our filter alongside the SEIF and the standard EKF both in simulation
as well as on two real-world datasets. While we maintain the computational
advantages of an exactly sparse representation, the results show convincingly that
our method yields conservative estimates for the robot pose and map that are nearly
identical to those of the original Gaussian distribution as produced by the EKF, but
at much less computational expense.
The thesis concludes with an extension of our SLAM filter to a complex underwater
environment. We describe a systems-level framework for localization and mapping
relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped
with a forward-looking sonar. The approach utilizes our filter to fuse measurements
of vehicle attitude and motion from onboard sensors with data from sonar images of
the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a
ship hull
The Estimation Methods for an Integrated INS/GPS UXO Geolocation System
This work was supported by a project funded by the US Army Corps of Engineers,
Strategic Environment Research and Development Program, contract number W912HQ-
08-C-0044.This report was also submitted to the Graduate School of the Ohio State
University in partial fulfillment of the PhD degree in Geodetic Science.Unexploded ordnance (UXO) is the explosive weapons such as mines, bombs, bullets,
shells and grenades that failed to explode when they were employed. In North America,
especially in the US, the UXO is the result of weapon system testing and troop training
by the DOD. The traditional UXO detection method employs metal detectors which
measure distorted signals of local magnetic fields. Based on detected magnetic signals,
holes are dug to remove buried UXO. However, the detection and remediation of UXO
contaminated sites using the traditional methods are extremely inefficient in that it is
difficult to distinguish the buried UXO from the noise of geologic magnetic sources or
anthropic clutter items. The reliable discrimination performance of UXO detection
system depends on the employed sensor technology as well as on the data processing
methods that invert the collected data to infer the UXO. The detection systems require
very accurate positioning (or geolocation) of the detection units to detect and discriminate
the candidate UXO from the non-hazardous clutter, greater position and orientation
precision because the inversion of magnetic or EMI data relies on their precise relative
locations, orientation, and depth. The requirements of position accuracy for MEC
geolocation and characterization using typical state-of-the-art detection instrumentation
are classified according to levels of accuracy outlined in: the screening level with position
tolerance of 0.5 m (as standard deviation), area mapping (less than 0.05 m), and
characterize and discriminate level of accuracy (less than 0.02m).
The primary geolocation system is considered as a dual-frequency GPS integrated with a
three dimensional inertial measurement unit (IMU); INS/GPS system. Selecting the
appropriate estimation method has been the key problem to obtain highly precise
geolocation of INS/GPS system for the UXO detection performance in dynamic
environments. For this purpose, the Extended Kalman Filter (EKF) has been used as the
conventional algorithm for the optimal integration of INS/GPS system. However, the
newly introduced non-linear based filters can deal with the non-linear nature of the
positioning dynamics as well as the non-Gaussian statistics for the instrument errors, and
the non-linear based estimation methods (filtering/smoothing) have been developed and
proposed. Therefore, this study focused on the optimal estimation methods for the
highly precise geolocation of INS/GPS system using simulations and analyses of two
Laboratory tests (cart-based and handheld geolocation system).
First, the non-linear based filters (UKF and UKF) have been shown to yield superior
performance than the EKF in various specific simulation tests which are designed similar
to the UXO geolocation environment (highly dynamic and small area). The UKF yields
50% improvement in the position accuracy over the EKF particularly in the curved
sections (medium-grade IMUs case). The UKF also performed significantly better than
EKF and shows comparable improvement over the UKF when the IMU noise probability
iii
density function is symmetric and non-symmetric. Also, since the UXO detection
survey does not require the real-time operations, each of the developed filters was
modified to accommodate the standard Rauch-Tung-Striebel (RTS) smoothing algorithms.
The smoothing methods are applied to the typical UXO detection trajectory; the position
error was reduced significantly using a minimal number of control points. Finally, these
simulation tests confirmed that tactical-grade IMUs (e.g. HG1700 or HG1900) are
required to bridge gaps of high-accuracy ranging solution systems longer than 1 second.
Second, these result of the simulation tests were validated from the laboratory tests using
navigation-grade and medium-grade accuracy IMUs. To overcome inaccurate a priori
knowledge of process noise of the system, the adaptive filtering methods have been
applied to the EKF and UKF and they are called the AEKS and AUKS. The neural
network aided adaptive nonlinear filtering/smoothing methods (NN-EKS and NN-UKS)
which are augmented with RTS smoothing method were compared with the AEKS and
AUKS. Each neural network-aided, adaptive filter/smoother improved the position
accuracy in both straight and curved sections. The navigation grade IMU (H764G) can
achieve the area mapping level of accuracy when the gap of control points is about 8
seconds. The medium grade IMUs (HG1700 and HG1900) with NN-AUKS can
maintain less than 10cm under the same conditions as above. Also, the neural network
aiding can decrease the difference of position error between the straight and the curved
section. Third, in the previous simulation test, the UPF performed better than the other
filters. However since the UPF needs a large number of samples to represent the a
posteriori statistics in high-dimensional space, the RBPF can be used as an alternative to
avoid the inefficiency of particle filter. The RBPF is tailored to precise geolocation for
UXO detection using IMU/GPS system and yielded improved estimation results with a
small number of samples. The handheld geolocation system using HG1900 with a
nonlinear filter-based smoother can achieve the discrimination level of accuracy if the
update rate of control points is less than 0.5Hz and 1Hz for the sweep and swing
respectively. Also, the sweep operation is more preferred than the swing motion
because the position accuracy of the sweep test was better than that of the swing test
Modeling and Control for Vision Based Rear Wheel Drive Robot and Solving Indoor SLAM Problem Using LIDAR
abstract: To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous
Machines operating in an uncertain Environment (FAME), this thesis addresses several
critical modeling, design, control objectives for rear-wheel drive ground vehicles.
Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform
that can be used for conducting FAME research.
A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.
The augmented vehicle (FreeSLAM Robot) costs less than 2000.
All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following
summarizes the key hardware demonstrations presented and analyzed:
(1)Cruise (v, ) control along a line,
(2) Cruise (v, ) control along a curve,
(3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle,
(4) Finish the track with camera pan tilt structure in minimum time,
(5) Finish the track without camera pan tilt structure in minimum time,
(6) Vision based tracking performance with different cruise speed vx,
(7) Vision based tracking performance with different camera fixed look-ahead distance L,
(8) Vision based tracking performance with different delay Td from vision subsystem,
(9) Manually remote controlled robot to perform indoor SLAM,
(10) Autonomously line guided robot to perform indoor SLAM.
For most cases, hardware data is compared with, and corroborated by, model based
simulation data. In short, the thesis uses low-cost self-designed rear-wheel
drive robot to demonstrate many capabilities that are critical in order to reach the
longer-term FAME goal.Dissertation/ThesisDefense PresentationMasters Thesis Electrical Engineering 201
Adaptive Localization and Mapping for Planetary Rovers
Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach