Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry

Li, Xin; Li, Yanyan; Örnek, Evin Pınar; Lin, Jianhua; Tombari, Federico

doi:10.1109/lra.2020.3027230

Cited by 24 publications

(10 citation statements)

References 34 publications

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“…Inspired by these works, we also merge point-to-plane constraints and extend them to homography observations. [22] forces the points associated with the plane to fall on the plane and uses the plane and the corresponding anchor pose to represent the point. This form of representation eliminates the point state and reduces the dimensionality of the Hessian matrix, which will increase BA solved speed.…”

Section: Rgb-d Inertial Slammentioning

confidence: 99%

“…Finally, the efficiency of bundle adjustment will be greatly improved through the smaller and sparser Hessian matrix. There is also a similar work [22] to remove the states of the plane points in optimization, but they use the reprojection representation method, it is difficult to compress multi-observation constraints, which limits the further improvement of the optimization speed. Homography associates the states of two keyframes and a plane.…”

Section: Back Endmentioning

confidence: 99%

“…So far, there are many literatures [13], [14], [15], [16], [17], [18], [19], [20], [21], [22] using line and planar features to improve the accuracy and robustness of SLAM in challenging environments. But most of them are just odometry or pure visual SLAM, and there is no literature to propose a complete RGB-D inertial plane tightly-coupled SLAM.…”

mentioning

confidence: 99%

“…But most of them are just odometry or pure visual SLAM, and there is no literature to propose a complete RGB-D inertial plane tightly-coupled SLAM. There is also work [22] discussing the use of planes to reduce the dimension of the Hession matrix and improve the solving speed of BA, but it is the only odometry, and re-parameterization does not reduce the number of constraints.…”

mentioning

confidence: 99%

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VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

Chen¹,

Wang²,

Xie³

et al. 2022

Preprint

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In this paper, we propose a tightly-coupled SLAM system fused with RGB, Depth, IMU and structured plane information. Traditional sparse points based SLAM systems always maintain a mass of map points to model the environment. Huge number of map points bring us a high computational complexity, making it difficult to be deployed on mobile devices. On the other hand, planes are common structures in man-made environment especially in indoor environments. We usually can use a small number of planes to represent a large scene. So the main purpose of this article is to decrease the high complexity of sparse points based SLAM. We build a lightweight backend map which consists of a few planes and map points to achieve efficient bundle adjustment (BA) with an equal or better accuracy. We use homography constraints to eliminate the parameters of numerous plane points in the optimization and reduce the complexity of BA. We separate the parameters and measurements in homography and point-to-plane constraints and compress the measurements part to further effectively improve the speed of BA. We also integrate the plane information into the whole system to realize robust planar feature extraction, data association, and global consistent planar reconstruction. Finally, we perform an ablation study and compare our method with similar methods in simulation and real environment data. Our system achieves obvious advantages in accuracy and efficiency. Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures. The global bundle adjustment is nearly 2 times faster than the sparse points based SLAM algorithm.

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Section: Rgb-d Inertial Slammentioning

confidence: 99%

Section: Back Endmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

Chen¹,

Wang²,

Xie³

et al. 2022

Preprint

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“…A direct frame-to-frame planar homography based VIO formulation was proposed in [27] for a downward-facing camera, but assumed a laser rangefinder for accurately estimating the scale. A recent optimization-based monocular VIO system used an efficient plane and line parameterization [28], while also leveraging a deep neural network for plane instance segmentation. However, all of these approaches have only been evaluated in static environments.…”

Section: B Monocular Vio Using Planesmentioning

confidence: 99%

RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments

Ram¹,

Kharyal²,

Harithas³

et al. 2021

Preprint

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Modern visual-inertial navigation systems (VINS) are faced with a critical challenge in real-world deployment: they need to operate reliably and robustly in highly dynamic environments. Current best solutions merely filter dynamic objects as outliers based on the semantics of the object category. Such an approach does not scale as it requires semantic classifiers to encompass all possibly-moving object classes; this is hard to define, let alone deploy. On the other hand, many realworld environments exhibit strong structural regularities in the form of planes such as walls and ground surfaces, which are also crucially static. We present RP-VIO, a monocular visual-inertial odometry system that leverages the simple geometry of these planes for improved robustness and accuracy in challenging dynamic environments. Since existing datasets have a limited number of dynamic elements, we also present a highly-dynamic, photorealistic synthetic dataset for a more effective evaluation of the capabilities of modern VINS systems. We evaluate our approach on this dataset, and three diverse sequences from standard datasets including two real-world dynamic sequences and show a significant improvement in robustness and accuracy over a state-of-the-art monocular visual-inertial odometry system. We also show in simulation an improvement over a simple dynamic-features masking approach. Our code and dataset are publicly available † .

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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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Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry

Cited by 24 publications

References 34 publications

VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments

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

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