11 research outputs found
A Quick Guide for the Iterated Extended Kalman Filter on Manifolds
The extended Kalman filter (EKF) is a common state estimation method for
discrete nonlinear systems. It recursively executes the propagation step as
time goes by and the update step when a set of measurements arrives. In the
update step, the EKF linearizes the measurement function only once. In
contrast, the iterated EKF (IEKF) refines the state in the update step by
iteratively solving a least squares problem. The IEKF has been extended to work
with state variables on manifolds which have differentiable and
operators, including Lie groups. However, existing descriptions are
often long, deep, and even with errors. This note provides a quick reference
for the IEKF on manifolds, using freshman-level matrix calculus. Besides the
bare-bone equations, we highlight the key steps in deriving them.Comment: 2 pages excluding reference
Consistent Right-Invariant Fixed-Lag Smoother with Application to Visual Inertial SLAM
State estimation problems without absolute position measurements routinely
arise in navigation of unmanned aerial vehicles, autonomous ground vehicles,
etc., whose proper operation relies on accurate state estimates and reliable
covariances. Unaware of absolute positions, these problems have immanent
unobservable directions. Traditional causal estimators, however, usually gain
spurious information on the unobservable directions, leading to over-confident
covariance inconsistent with actual estimator errors. The consistency problem
of fixed-lag smoothers (FLSs) has only been attacked by the first estimate
Jacobian (FEJ) technique because of the complexity to analyze their
observability property. But the FEJ has several drawbacks hampering its wide
adoption. To ensure the consistency of a FLS, this paper introduces the right
invariant error formulation into the FLS framework. To our knowledge, we are
the first to analyze the observability of a FLS with the right invariant error.
Our main contributions are twofold. As the first novelty, to bypass the
complexity of analysis with the classic observability matrix, we show that
observability analysis of FLSs can be done equivalently on the linearized
system. Second, we prove that the inconsistency issue in the traditional FLS
can be elegantly solved by the right invariant error formulation without
artificially correcting Jacobians. By applying the proposed FLS to the
monocular visual inertial simultaneous localization and mapping (SLAM) problem,
we confirm that the method consistently estimates covariance similarly to a
batch smoother in simulation and that our method achieved comparable accuracy
as traditional FLSs on real data.Comment: 13 pages, 4 figures, AAAI 2021 Conferenc
3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving
For the SLAM system in robotics and autonomous driving, the accuracy of
front-end odometry and back-end loop-closure detection determine the whole
intelligent system performance. But the LiDAR-SLAM could be disturbed by
current scene moving objects, resulting in drift errors and even loop-closure
failure. Thus, the ability to detect and segment moving objects is essential
for high-precision positioning and building a consistent map. In this paper, we
address the problem of moving object segmentation from 3D LiDAR scans to
improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D
Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately
segment the scene into moving and static objects, such as moving and static
cars. Different from the existing projected-image method, we process the raw 3D
point cloud and build a 3D convolution neural network for MOS task. In
addition, to make full use of the spatio-temporal information of point cloud,
we propose a point cloud residual mechanism using the spatial features of
current scan and the temporal features of previous residual scans. Besides, we
build a complete SLAM framework to verify the effectiveness and accuracy of
3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed
3D-SeqMOS method can effectively detect moving objects and improve the accuracy
of LiDAR odometry and loop-closure detection. The test results show our
3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the
proposed method to the SemanticKITTI: Moving Object Segmentation competition
and achieve the 2nd in the leaderboard, showing its effectiveness
Coexistence and interference mitigation for WPANs and WLANs from traditional approaches to deep learning: a review
More and more devices, such as Bluetooth and IEEE 802.15.4 devices forming Wireless Personal Area Networks (WPANs) and IEEE 802.11 devices constituting Wireless Local Area Networks (WLANs), share the 2.4 GHz Industrial, Scientific and Medical (ISM) band in the realm of the Internet of Things (IoT) and Smart Cities. However, the coexistence of these devices could pose a real challenge—co-channel interference that would severely compromise network performances. Although the coexistence issues has been partially discussed elsewhere in some articles, there is no single review that fully summarises and compares recent research outcomes and challenges of IEEE 802.15.4 networks, Bluetooth and WLANs together. In this work, we revisit and provide a comprehensive review on the coexistence and interference mitigation for those three types of networks. We summarize the strengths and weaknesses of the current methodologies, analysis and simulation models in terms of numerous important metrics such as the packet reception ratio, latency, scalability and energy efficiency. We discover that although Bluetooth and IEEE 802.15.4 networks are both WPANs, they show quite different performances in the presence of WLANs. IEEE 802.15.4 networks are adversely impacted by WLANs, whereas WLANs are interfered by Bluetooth. When IEEE 802.15.4 networks and Bluetooth co-locate, they are unlikely to harm each other. Finally, we also discuss the future research trends and challenges especially Deep-Learning and Reinforcement-Learning-based approaches to detecting and mitigating the co-channel interference caused by WPANs and WLANs
demo.mp4
This video demonstrates our rolling shutter calibration method on datasets captured by a variety of cameras, including industrial cameras, smartphone cameras, and cameras on 3D sensors