3,663 research outputs found
Visual-inertial self-calibration on informative motion segments
Environmental conditions and external effects, such as shocks, have a
significant impact on the calibration parameters of visual-inertial sensor
systems. Thus long-term operation of these systems cannot fully rely on factory
calibration. Since the observability of certain parameters is highly dependent
on the motion of the device, using short data segments at device initialization
may yield poor results. When such systems are additionally subject to energy
constraints, it is also infeasible to use full-batch approaches on a big
dataset and careful selection of the data is of high importance. In this paper,
we present a novel approach for resource efficient self-calibration of
visual-inertial sensor systems. This is achieved by casting the calibration as
a segment-based optimization problem that can be run on a small subset of
informative segments. Consequently, the computational burden is limited as only
a predefined number of segments is used. We also propose an efficient
information-theoretic selection to identify such informative motion segments.
In evaluations on a challenging dataset, we show our approach to significantly
outperform state-of-the-art in terms of computational burden while maintaining
a comparable accuracy
Observability-aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation
External effects such as shocks and temperature variations affect the
calibration of visual-inertial sensor systems and thus they cannot fully rely
on factory calibrations. Re-calibrations performed on short user-collected
datasets might yield poor performance since the observability of certain
parameters is highly dependent on the motion. Additionally, on
resource-constrained systems (e.g mobile phones), full-batch approaches over
longer sessions quickly become prohibitively expensive.
In this paper, we approach the self-calibration problem by introducing
information theoretic metrics to assess the information content of trajectory
segments, thus allowing to select the most informative parts from a dataset for
calibration purposes. With this approach, we are able to build compact
calibration datasets either: (a) by selecting segments from a long session with
limited exciting motion or (b) from multiple short sessions where a single
sessions does not necessarily excite all modes sufficiently. Real-world
experiments in four different environments show that the proposed method
achieves comparable performance to a batch calibration approach, yet, at a
constant computational complexity which is independent of the duration of the
session
Accelerometer calibration for NASA\u27s magnetospheric multiscale mission spacecraft
This thesis presents several methods for the on-board and/or ground-based calibration of accelerometers for the spacecraft (s/c) of the NASA Magnetospheric Multi-Scale (MMS) Mission during mission operation. A lumped bias is estimated to correct for the total effect of the MMS accelerometer sensor bias, orthogonal misalignment and the shift in the s/c\u27s center of mass.
Various estimation techniques are evaluated and compared, including both dynamically driven real-time filters/observers and post processing batch algorithms. Both methods are shown to accurately determine lumped bias, so long as the s/c inertia tensor is well known. If, however, there is any uncertainty in the inertia tensor, only post processing methods yield accurate lumped bias estimates. Analytical simulations show that these methods are able to correct accelerometer readings to within 1 micro-g of true acceleration. Preliminary experimental verification also shows proof of concept
Implementation of a robotic flexible assembly system
As part of the Intelligent Task Automation program, a team developed enabling technologies for programmable, sensory controlled manipulation in unstructured environments. These technologies include 2-D/3-D vision sensing and understanding, force sensing and high speed force control, 2.5-D vision alignment and control, and multiple processor architectures. The subsequent design of a flexible, programmable, sensor controlled robotic assembly system for small electromechanical devices is described using these technologies and ongoing implementation and integration efforts. Using vision, the system picks parts dumped randomly in a tray. Using vision and force control, it performs high speed part mating, in-process monitoring/verification of expected results and autonomous recovery from some errors. It is programmed off line with semiautomatic action planning
FINDS: A fault inferring nonlinear detection system. User's guide
The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system
Flight Mechanics/Estimation Theory Symposium, 1990
This conference publication includes 32 papers and abstracts presented at the Flight Mechanics/Estimation Theory Symposium on May 22-25, 1990. Sponsored by the Flight Dynamics Division of Goddard Space Flight Center, this symposium features technical papers on a wide range of issues related to orbit-attitude prediction, determination and control; attitude sensor calibration; attitude determination error analysis; attitude dynamics; and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers
A Comprehensive Introduction of Visual-Inertial Navigation
In this article, a tutorial introduction to visual-inertial navigation(VIN)
is presented. Visual and inertial perception are two complementary sensing
modalities. Cameras and inertial measurement units (IMU) are the corresponding
sensors for these two modalities. The low cost and light weight of camera-IMU
sensor combinations make them ubiquitous in robotic navigation. Visual-inertial
Navigation is a state estimation problem, that estimates the ego-motion and
local environment of the sensor platform. This paper presents visual-inertial
navigation in the classical state estimation framework, first illustrating the
estimation problem in terms of state variables and system models, including
related quantities representations (Parameterizations), IMU dynamic and camera
measurement models, and corresponding general probabilistic graphical models
(Factor Graph). Secondly, we investigate the existing model-based estimation
methodologies, these involve filter-based and optimization-based frameworks and
related on-manifold operations. We also discuss the calibration of some
relevant parameters, also initialization of state of interest in
optimization-based frameworks. Then the evaluation and improvement of VIN in
terms of accuracy, efficiency, and robustness are discussed. Finally, we
briefly mention the recent development of learning-based methods that may
become alternatives to traditional model-based methods.Comment: 35 pages, 10 figure
Flight test results of the strapdown hexad inertial reference unit (SIRU). Volume 2: Test report
Results of flight tests of the Strapdown Inertial Reference Unit (SIRU) navigation system are presented. The fault tolerant SIRU navigation system features a redundant inertial sensor unit and dual computers. System software provides for detection and isolation of inertial sensor failures and continued operation in the event of failures. Flight test results include assessments of the system's navigational performance and fault tolerance. Performance shortcomings are analyzed
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