186 research outputs found
Multi-Visual-Inertial System: Analysis, Calibration and Estimation
In this paper, we study state estimation of multi-visual-inertial systems
(MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary
number of asynchronous inertial measurement units (IMUs) or gyroscopes and
global and(or) rolling shutter cameras. We are especially interested in the
full calibration of the associated visual-inertial sensors, including the IMU
or camera intrinsics and the IMU-IMU(or camera) spatiotemporal extrinsics as
well as the image readout time of rolling-shutter cameras (if used). To this
end, we develop a new analytic combined IMU integration with intrinsics-termed
ACI3-to preintegrate IMU measurements, which is leveraged to fuse auxiliary
IMUs and(or) gyroscopes alongside a base IMU. We model the multi-inertial
measurements to include all the necessary inertial intrinsic and IMU-IMU
spatiotemporal extrinsic parameters, while leveraging IMU-IMU rigid-body
constraints to eliminate the necessity of auxiliary inertial poses and thus
reducing computational complexity. By performing observability analysis of
MVIS, we prove that the standard four unobservable directions remain - no
matter how many inertial sensors are used, and also identify, for the first
time, degenerate motions for IMU-IMU spatiotemporal extrinsics and auxiliary
inertial intrinsics. In addition to the extensive simulations that validate our
analysis and algorithms, we have built our own MVIS sensor rig and collected
over 25 real-world datasets to experimentally verify the proposed calibration
against the state-of-the-art calibration method such as Kalibr. We show that
the proposed MVIS calibration is able to achieve competing accuracy with
improved convergence and repeatability, which is open sourced to better benefit
the community
Keyframe-based visual–inertial odometry using nonlinear optimization
Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate visual–inertial odometry or simultaneous localization and mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that nonlinear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual–inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochastic cloning sliding-window filter. This competitive reference implementation performs tightly coupled filtering-based visual–inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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
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