6,445 research outputs found
AlphaPilot: Autonomous Drone Racing
This paper presents a novel system for autonomous, vision-based drone racing
combining learned data abstraction, nonlinear filtering, and time-optimal
trajectory planning. The system has successfully been deployed at the first
autonomous drone racing world championship: the 2019 AlphaPilot Challenge.
Contrary to traditional drone racing systems, which only detect the next gate,
our approach makes use of any visible gate and takes advantage of multiple,
simultaneous gate detections to compensate for drift in the state estimate and
build a global map of the gates. The global map and drift-compensated state
estimate allow the drone to navigate through the race course even when the
gates are not immediately visible and further enable to plan a near
time-optimal path through the race course in real time based on approximate
drone dynamics. The proposed system has been demonstrated to successfully guide
the drone through tight race courses reaching speeds up to 8m/s and ranked
second at the 2019 AlphaPilot Challenge.Comment: Accepted at Robotics: Science and Systems 2020, associated video at
https://youtu.be/DGjwm5PZQT
Contributions to automated realtime underwater navigation
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 2012This dissertation presents three separate–but related–contributions to the art of underwater
navigation. These methods may be used in postprocessing with a human in
the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is
on automated approaches that can be used in realtime. The three research threads
are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column,
and iii) model-driven delayed measurement fusion. Contributions to each of
these areas have been demonstrated in simulation, with laboratory data, or in the
field–some have been demonstrated in all three arenas.
The solution to the in situ navigation sensor alignment problem is an asymptotically
stable adaptive identifier formulated using rotors in Geometric Algebra. This
identifier is applied to precisely estimate the unknown alignment between a gyrocompass
and Doppler velocity log, with the goal of improving realtime dead reckoning
navigation. Laboratory and field results show the identifier performs comparably to
previously reported methods using rotation matrices, providing an alignment estimate
that reduces the position residuals between dead reckoning and an external acoustic
positioning system. The Geometric Algebra formulation also encourages a straightforward
interpretation of the identifier as a proportional feedback regulator on the
observable output error. Future applications of the identifier may include alignment
between inertial, visual, and acoustic sensors.
The ability to link the Global Positioning System at the surface to precision dead
reckoning near the seafloor might enable new kinds of missions for autonomous underwater
vehicles. This research introduces a method for dead reckoning through
the water column using water current profile data collected by an onboard acoustic
Doppler current profiler. Overlapping relative current profiles provide information to
simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity
is then integrated to estimate position. The method is applied to field data using
online bin average, weighted least squares, and recursive least squares implementations.
This demonstrates an autonomous navigation link between the surface and the
seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an
interesting challenge. Underwater navigation is a particularly compelling case because
of the relatively long delays inherent in all available position measurements. This research
develops a flexible, model-driven approach to delayed measurement fusion in
realtime Kalman filters. Using a priori estimates of delayed measurements as augmented
states minimizes the computational cost of the delay treatment. Managing
the augmented states with time-varying conditional process and measurement models
ensures the approach works within the proven Kalman filter framework–without
altering the filter structure or requiring any ad-hoc adjustments. The end result is
a mathematically principled treatment of the delay that leads to more consistent estimates
with lower error and uncertainty. Field results from dead reckoning aided
by acoustic positioning systems demonstrate the applicability of this approach to
real-world problems in underwater navigation.I have been financially supported by:
the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered
by the American Society for Engineering Education, the Edwin A. Link
Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic
Programs office
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
Current approaches for visual-inertial odometry (VIO) are able to attain
highly accurate state estimation via nonlinear optimization. However, real-time
optimization quickly becomes infeasible as the trajectory grows over time, this
problem is further emphasized by the fact that inertial measurements come at
high rate, hence leading to fast growth of the number of variables in the
optimization. In this paper, we address this issue by preintegrating inertial
measurements between selected keyframes into single relative motion
constraints. Our first contribution is a \emph{preintegration theory} that
properly addresses the manifold structure of the rotation group. We formally
discuss the generative measurement model as well as the nature of the rotation
noise and derive the expression for the \emph{maximum a posteriori} state
estimator. Our theoretical development enables the computation of all necessary
Jacobians for the optimization and a-posteriori bias correction in analytic
form. The second contribution is to show that the preintegrated IMU model can
be seamlessly integrated into a visual-inertial pipeline under the unifying
framework of factor graphs. This enables the application of
incremental-smoothing algorithms and the use of a \emph{structureless} model
for visual measurements, which avoids optimizing over the 3D points, further
accelerating the computation. We perform an extensive evaluation of our
monocular \VIO pipeline on real and simulated datasets. The results confirm
that our modelling effort leads to accurate state estimation in real-time,
outperforming state-of-the-art approaches.Comment: 20 pages, 24 figures, accepted for publication in IEEE Transactions
on Robotics (TRO) 201
Location-dependent information extraction for positioning
This paper presents an overview of current research investigations within the WHERE-2 Project with respect to location-dependent information extraction and how this information can be used towards the benefit of positioning. It is split into two main sections; the first one relies on non-radio means such as inertial sensors and prior knowledge about the environment geometry, which can be used in the form of map constraints to improve user positioning precision in indoor environments. The second section presents how location-specific radio information can be exploited in a more sophisticated way into advanced positioning algorithms. The intended solutions include exploitation of the slow fading dynamics in addition to the fast-fading parameters, adaptation of the system to its environment on both network and terminal sides and also how specific environmental properties such as the dielectric wall parameters can be extracted and thereafter used for more accurate fingerprinting database generation using Ray Tracing modelling methods. Most of the techniques presented herein rely on real-life measurements or experiments
Signal Processing in Cold Atom Interferometry-Based INS
High precision Cold Atom Interferometers (CAI) are in development to supplement or replace conventional, navigation quality inertial measurement units. A major drawback of the atomic interferometers is their low duty cycle and sampling rate, caused by delays required for cooling the atoms and collecting acceleration and angular rate measurements. A method is herein developed for inertial navigation by integrating highly accurate, low duty cycle CAI measurements with high bandwidth, conventional Inertial Navigation System (INS) measurements. A xed-lag smoothing algorithm is used to estimate optimal acceleration and angular rate measurements from the CAI and INS data. Given current CAI limitations, simulation results demonstrate nearly 50 percent error reduction for the enhanced INS compared to a conventional, unaided INS. When the conventional INS position error was increased by 500 (m/hr), the 50 percent error reduction from aiding was maintained. Increasing the conventional INS data rate fifteen-fold while maintaining a 1 Hz CAI sample rate leads to an approximately 6 percent increase in navigation error, suggesting that the CAI- aiding algorithm effectivity is only slightly influenced by the conventional INS data rates. A five-fold increase of the CAI measurement rate shows approximately 80 percent reduction in navigation error, supporting the potential for significant performance gains in the near future from advancements in cold atom technology
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ECEF Position Accuracy and Reliability: Inertial Navigation with GNSS Precise Point Positioning (PPP)
This report presents experimental results for a moving platform using GPS PPP data for state estimation. Results from two PPP GPS state estimation approaches are presented: point-wise least squares (LS) and aided inertial navigation (INS). The point-wise LS results provide information about the accuracy and reliability of PPP GPS information at each measurement epoch, independent of other epochs. The INS results show the performance that can be achieved by combining information across measurement epochs. INS results are included for two different grades of IMU: navigation grade and consumer grade.The report cites publications that contain more detailed expla- nations of the GNSS error sources, computation of PPP wide area correction, and the LS and aided INS estimation algorithms
Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation
A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows:
i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches.
ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements.
iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector
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