1,607 research outputs found
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data
In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud
representation of the scene that does not model the topology of the
environment. A 3D mesh instead offers a richer, yet lightweight, model.
Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks
triangulated by a VIO algorithm often results in a mesh that does not fit the
real scene. In order to regularize the mesh, previous approaches decouple state
estimation from the 3D mesh regularization step, and either limit the 3D mesh
to the current frame or let the mesh grow indefinitely. We propose instead to
tightly couple mesh regularization and state estimation by detecting and
enforcing structural regularities in a novel factor-graph formulation. We also
propose to incrementally build the mesh by restricting its extent to the
time-horizon of the VIO optimization; the resulting 3D mesh covers a larger
portion of the scene than a per-frame approach while its memory usage and
computational complexity remain bounded. We show that our approach successfully
regularizes the mesh, while improving localization accuracy, when structural
regularities are present, and remains operational in scenes without
regularities.Comment: 7 pages, 5 figures, ICRA accepte
Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM
Sparsity has been widely recognized as crucial for efficient optimization in
graph-based SLAM. Because the sparsity and structure of the SLAM graph reflect
the set of incorporated measurements, many methods for sparsification have been
proposed in hopes of reducing computation. These methods often focus narrowly
on reducing edge count without regard for structure at a global level. Such
structurally-naive techniques can fail to produce significant computational
savings, even after aggressive pruning. In contrast, simple heuristics such as
measurement decimation and keyframing are known empirically to produce
significant computation reductions. To demonstrate why, we propose a
quantitative metric called elimination complexity (EC) that bridges the
existing analytic gap between graph structure and computation. EC quantifies
the complexity of the primary computational bottleneck: the factorization step
of a Gauss-Newton iteration. Using this metric, we show rigorously that
decimation and keyframing impose favorable global structures and therefore
achieve computation reductions on the order of and , respectively,
where is the pruning rate. We additionally present numerical results
showing EC provides a good approximation of computation in both batch and
incremental (iSAM2) optimization and demonstrate that pruning methods promoting
globally-efficient structure outperform those that do not.Comment: Pre-print accepted to ICRA 201
Flight-test evaluation of STOL control and flight director concepts in a powered-lift aircraft flying curved decelerating approaches
Flight tests were carried out to assess the feasibility of piloted steep curved, and decelerating approach profiles in powered lift STOL aircraft. Several STOL control concepts representative of a variety of aircraft were evaluated in conjunction with suitably designed flight directions. The tests were carried out in a real navigation environment, employed special electronic cockpit displays, and included the development of the performance achieved and the control utilization involved in flying 180 deg turning, descending, and decelerating approach profiles to landing. The results suggest that such moderately complex piloted instrument approaches may indeed be feasible from a pilot acceptance point of view, given an acceptable navigation environment. Systems with the capability of those used in this experiment can provide the potential of achieving instrument operations on curved, descending, and decelerating landing approaches to weather minima corresponding to CTOL Category 2 criteria, while also providing a means of realizing more efficient operations during visual flight conditions
Editorial: special issue on autonomous driving and driver assistance systems
No abstract availablepublishe
Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data
In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing
SPINS: Structure Priors aided Inertial Navigation System
Although Simultaneous Localization and Mapping (SLAM) has been an active
research topic for decades, current state-of-the-art methods still suffer from
instability or inaccuracy due to feature insufficiency or its inherent
estimation drift, in many civilian environments. To resolve these issues, we
propose a navigation system combing the SLAM and prior-map-based localization.
Specifically, we consider additional integration of line and plane features,
which are ubiquitous and more structurally salient in civilian environments,
into the SLAM to ensure feature sufficiency and localization robustness. More
importantly, we incorporate general prior map information into the SLAM to
restrain its drift and improve the accuracy. To avoid rigorous association
between prior information and local observations, we parameterize the prior
knowledge as low dimensional structural priors defined as relative
distances/angles between different geometric primitives. The localization is
formulated as a graph-based optimization problem that contains
sliding-window-based variables and factors, including IMU, heterogeneous
features, and structure priors. We also derive the analytical expressions of
Jacobians of different factors to avoid the automatic differentiation overhead.
To further alleviate the computation burden of incorporating structural prior
factors, a selection mechanism is adopted based on the so-called information
gain to incorporate only the most effective structure priors in the graph
optimization. Finally, the proposed framework is extensively tested on
synthetic data, public datasets, and, more importantly, on the real UAV flight
data obtained from a building inspection task. The results show that the
proposed scheme can effectively improve the accuracy and robustness of
localization for autonomous robots in civilian applications.Comment: 14 pages, 14 figure
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