On-Manifold Preintegration for Real-Time Visual--Inertial Odometry

By Christian Forster, Luca Carlone, Frank Dellaert, Davide Scaramuzza. IEEE Transactions on robotics, 2015

1,330 citations recorded by the source corpus.

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 the 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 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 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 inertial measurement unit 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 structureless model for visual measurements, which avoids optimizing over the 3-D 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 modeling effort leads to an accurate state estimation in real time, outperforming state-of-the-art approaches.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/215fe6ebcf44a364edca5e9a19ff79d3360d0e86

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Citations in the corpus

Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning (2024, methodology): “The propagation of covariance and approximate bias up date techniques are also adopted in our system, which are detailed in [ 11].”
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, methodology): “Asweareoptimizing in a manifold we need to deﬁne a retraction [62] to update the gravity direction estimation during the optimization: R newwg = R oldwg Exp ( δα g , δβ g , 0) being Exp ( .”
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry (2020, neutral): “25 SVO [22] 0.”
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems (2021, methodology): “In order to evaluate estimation accuracy in the absence of ground truth trajectories, we measure end-to-end errors as in [95].”
Contact-aided invariant extended Kalman filtering for robot state estimation (2019, methodology): “The theory of invariant observer design is based on the estimation error being invariant under the action of a matrix Lie group (Aghannan and Rouchon, 2002; Bonnabel et al., 2009), which has recently led to the development of the InEKF1 (Barrau, 2015; Barrau and Bonnabel, 2017, 2018; Bonnabel, 2007)”
RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods (2019, background): “IMU sensor fusion produces real-time orientations of Virtual Reality headsets, augments SLAM [1]–[3] in Augmented Reality [4]–[6], and allows enhanced navigation and control in many domains.”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, positive): “et of linear equations (the so called normal equations). These approaches can be seamlessly generalized to variables belonging to smooth manifolds (e.g., rotations), which are of interest in robotics [1, 86]. The key insight behind modern SLAM solvers is that the matrix appearing in the normal equat”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “preintegration and fixed-lag smoothing (Forster et al. 2017), (ii) Kimera-PGMO: a robust pose graph and mesh optimizer, that generalizes Kimera-RPGO, which only optimized the pose graph.”
Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios (2017, methodology): “A recent approach in this category was proposed by [3].”
ESIM: an Open Event Camera Simulator (2018, methodology): “Following the notation of [25], we denote the pose of the sensor expressed in some inertial frame W by T WB ∈ SE (3) , where SE (3) denotes the group of rigid motion in 3D, the twist by ξ ( t ) = ( W v ( t ) , B ω WB ( t )) , and its acceleration (expressed in the inertial frame W ) by B a ( t ) .”
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors (2017, methodology): “is the relative rotation increment from the IMU preintegration model [14].”
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (2020, methodology): “Due to space limitations, we refer the reader to the description from [20] for the detailed derivation of Eqs.”
GVINS: Tightly Coupled GNSS–Visual–Inertial Fusion for Smooth and Consistent State Estimation (2021, methodology): “To this end, IMU pre-integration approach [26] is adopted to aggregate multiple measurements into a single one.”
A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry (2018, methodology): “In this paper, we use the preintegrated IMU measurements proposed in [9], [10].”
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator (2017, methodology): “We note that our current IMU preintegration procedure shares almost the same numerical results as [19] and [24], but using different derivations.”
Visual-Inertial Navigation: A Concise Review (2019, background): “was introduced in [29, 34] which presents a singularity-free”