Robust and Efficient Visual-Inertial Odometry with Multi-plane Priors

Li, Jinyu; Yang, Bangbang; Huang, Kai; Bao, Hujun

doi:10.1007/978-3-030-31726-3_24

Cited by 10 publications

(12 citation statements)

References 19 publications

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“…In the EuRoC dataset, all the MH sequences contain a long idle period in the beginning. Compared with our previous PVIO, 1 RLP‐VIO can keep the pose stable and survive the depth starvation while PVIO shows jittering, as shown in Figure 7.…”

Section: Methodsmentioning

confidence: 80%

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RLP‐VIO: Robust and lightweight plane‐based visual‐inertial odometry for augmented reality

Zhou

Yang

et al. 2022

Computer Animation & Virtual

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We propose RLP-VIO-a robust and lightweight monocular visual-inertial odometry system using multiplane priors. With planes extracted from the point cloud, visual-inertial-plane PnP uses the plane information for fast localization. Depth estimation is susceptible to degenerated motion, so the planes are expanded in a reprojection consensus-based way robust to depth errors.For sensor fusion, our sliding-window optimization uses a novel structureless plane-distance error cost, which prevents the fill-in effect that poisons the BA problem's sparsity and permits the use of a smaller sliding window while maintaining good accuracy. The total computational cost is further reduced with our modified marginalization strategy. To further improve the tracking robustness, the landmark depths are constrained using the planes during degenerated motion. The whole system is parallelized with a three-stage pipeline. Under controlled environments, this parallelization runs deterministically and produces consistent results. The resulting VIO system is tested on widely used datasets and compared with several state-of-the-art systems. Our system achieves competitive accuracy and works robustly even on long and challenging sequences.To demonstrate the effectiveness of the proposed system, we also show the AR application running on mobile devices in real-time.

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Section: Methodsmentioning

confidence: 80%

“…The structureless cost is much easier to evaluate, and the sparse structure of the bundle adjustment is respected. Based on our prior work, 1 we made two additional improvements. First, under small‐translation movements, we use planes for stabilizing the depths.…”

Section: Introductionmentioning

confidence: 99%

RLP‐VIO: Robust and lightweight plane‐based visual‐inertial odometry for augmented reality

Zhou

Yang

et al. 2022

Computer Animation & Virtual

Self Cite

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“…Compared with the loosely coupled method, the tightly coupled method can obtain higher trajectory accuracy [ 10 , 11 ]. The state variables in the tightly coupled approach will become continually larger in size with the operation of the algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…With the increasing requirements for localization accuracy and robustness in different application scenarios, it is not enough to collect information from a single sensor and then calculate the position. Therefore, more and more researchers are focusing on multi-source fusion approaches [ 11 ]. There are various ways of using multi-source fusion.…”

Section: Introductionmentioning

confidence: 99%

A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel

Zhu

et al. 2022

Sensors

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Mobile robots moving fast or in scenes with poor lighting conditions often cause the loss of visual feature tracking. In coal mine tunnels, the ground is often bumpy and the lighting is uneven. During the movement of the mobile robot in this scene, there will be violent bumps. The localization technology through visual features is greatly affected by the illumination and the speed of the camera movement. To solve the localization and mapping problem in an environment similar to underground coal mine tunnels, we improve a localization and mapping algorithm based on a monocular camera and an Inertial Measurement Unit (IMU). A feature-matching method that combines point and line features is designed to improve the robustness of the algorithm in the presence of degraded scene structure and insufficient illumination. The tightly coupled method is used to establish visual feature constraints and IMU pre-integration constraints. A keyframe nonlinear optimization algorithm based on sliding windows is used to accomplish state estimation. Extensive simulations and practical environment verification show that the improved simultaneous localization and mapping (SLAM) system with a monocular camera and IMU fusion can achieve accurate autonomous localization and map construction in scenes with insufficient light such as coal mine tunnels.

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“…To overcome the disadvantages, several online methods were developed. The online methods assumed that the measurements from a camera and an IMU were well synchronized (e.g., [14][15][16][17][18][19]), or the extrinsic spatial parameter was known in advance (e.g., [20][21][22]), or both conditions were satisfied (e.g., [23][24][25][26][27][28]). In the case where both the measurements from different sensors are asynchronous and the extrinsic spatial parameter between different sensors is unknown, most of the existing methods [29][30][31][32][33][34] are designed for filter-based VIOs since they are usually built on the Multi-State Constraint Kalman Filter (MSCKF [35]) framework.…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Huang

Wan

Liu

2021

Sensors

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The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

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Robust and Efficient Visual-Inertial Odometry with Multi-plane Priors

Cited by 10 publications

References 19 publications

RLP‐VIO: Robust and lightweight plane‐based visual‐inertial odometry for augmented reality

RLP‐VIO: Robust and lightweight plane‐based visual‐inertial odometry for augmented reality

A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

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