DM-VIO: Delayed Marginalization Visual-Inertial Odometry

• URL: http://arxiv.org/abs/2201.04114v1
• Date: Tue, 11 Jan 2022 18:30:37 GMT
• Title: DM-VIO: Delayed Marginalization Visual-Inertial Odometry
• Authors: Lukas von Stumberg, Daniel Cremers
• Abstract summary: We present DM-VIO, a visual-inertial system based on delayed marginalization and pose graph bundle adjustment. We evaluate our system on the EuRoC, TUM-VI, and 4Seasons datasets, which comprise flying drone, large-scale handheld, and automotive scenarios.
• Score: 62.746533939737446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DM-VIO, a monocular visual-inertial odometry system based on two novel techniques called delayed marginalization and pose graph bundle adjustment. DM-VIO performs photometric bundle adjustment with a dynamic weight for visual residuals. We adopt marginalization, which is a popular strategy to keep the update time constrained, but it cannot easily be reversed, and linearization points of connected variables have to be fixed. To overcome this we propose delayed marginalization: The idea is to maintain a second factor graph, where marginalization is delayed. This allows us to later readvance this delayed graph, yielding an updated marginalization prior with new and consistent linearization points. In addition, delayed marginalization enables us to inject IMU information into already marginalized states. This is the foundation of the proposed pose graph bundle adjustment, which we use for IMU initialization. In contrast to prior works on IMU initialization, it is able to capture the full photometric uncertainty, improving the scale estimation. In order to cope with initially unobservable scale, we continue to optimize scale and gravity direction in the main system after IMU initialization is complete. We evaluate our system on the EuRoC, TUM-VI, and 4Seasons datasets, which comprise flying drone, large-scale handheld, and automotive scenarios. Thanks to the proposed IMU initialization, our system exceeds the state of the art in visual-inertial odometry, even outperforming stereo-inertial methods while using only a single camera and IMU. The code will be published at http://vision.in.tum.de/dm-vio

Related papers

• EqNIO: Subequivariant Neural Inertial Odometry [33.96552018734359]
  We show that IMU data transforms equivariantly, when rotated around the gravity vector and reflected with respect to arbitrary planes parallel to gravity. We then map the IMU data into this frame, thereby achieving an invariant canonicalization that can be directly used with off-the-shelf inertial odometry networks.
  arXiv  Detail & Related papers  (2024-08-12T17:42:46Z)
• Implicit Bias and Fast Convergence Rates for Self-attention [30.08303212679308]
  Self-attention, the core mechanism of transformers, distinguishes them from traditional neural networks and drives their outstanding performance. We investigate the implicit bias of gradient descent (GD) in training a self-attention layer with fixed linear decoder in binary. We provide the first finite-time convergence rate for $W_t$ to $W_mm$, along with the rate of sparsification in the attention map.
  arXiv  Detail & Related papers  (2024-02-08T15:15:09Z)
• RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments [55.864869961717424]
  It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. We design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these problems.
  arXiv  Detail & Related papers  (2023-10-23T16:30:39Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution [112.3443939502313]
  We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain.
  arXiv  Detail & Related papers  (2023-02-08T08:17:43Z)
• Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography [58.60249163402822]
  Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. The proposed OMR is more robust and performs significantly better than the previous state-of-the-art OMR approach.
  arXiv  Detail & Related papers  (2022-07-06T21:40:59Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)
• Renormalization for Initialization of Rolling Shutter Visual-Inertial Odometry [5.33024001730262]
  Initialization is a prerequisite for using inertial signals and fusing them with visual data. We propose a novel statistical solution to the problem on visual and inertial data simultaneously, by casting it into the renormalization scheme of Kanatani. Extensive evaluations on ground truth exhibit superior performance and a gain in accuracy of up to $20%$ over the originally proposed Least Squares solution.
  arXiv  Detail & Related papers  (2020-08-14T14:54:15Z)
• Online Initialization and Extrinsic Spatial-Temporal Calibration for Monocular Visual-Inertial Odometry [19.955414423860788]
  This paper presents an online method for bootstrapping the optimization-based monocular visual-inertial odometry (VIO) The method can online calibrate the relative transformation (spatial) and time offsets (temporal) among camera and IMU, as well as estimate the initial values of metric scale, velocity, gravity, gyroscope bias, and accelerometer bias. Experimental results on public datasets show that the initial values and the parameters, as well as the sensor poses, can be accurately estimated by the proposed method.
  arXiv  Detail & Related papers  (2020-04-12T03:13:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.