<i>DynaVINS:</i> A Visual-Inertial SLAM for Dynamic Environments

Song, Seungwon; Lim, Hyungtae; Lee, Alex Junho; Myung, Hyun

doi:10.1109/lra.2022.3203231

Cited by 64 publications

(23 citation statements)

References 28 publications

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“…Ren et al [5] proposes a dense RGB-D-inertial SLAM system that can track and relocalise multiple dynamic objects with the aid of instance segmentation from Mask R-CNN [12]. In contrast, DynaVINS [8] can remove undefined dynamic objects that are dominant in the visual input using the camera motion priors from a low-cost IMU. It also actively detects temporarily static objects to reject false loop closure constraints.…”

Section: B Proprioception-aided Slammentioning

confidence: 99%

“…Various dynamic SLAM methods [6], [7], [8] have explicitly considered dynamic objects that are dominant in the camera views with the aid of robot proprioception, like wheel odometry or Inertial Measurement Units (IMU). Some static visual-inertial navigation system (VINS) methods [9], [10] have also demonstrated their robustness when the camera view is fully occluded for a short period.…”

Section: Introductionmentioning

confidence: 99%

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RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

Long¹,

Rauch²,

Ivan³

et al. 2023

Preprint

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This work presents a novel RGB-D-inertial dynamic SLAM method that can enable accurate localisation when the majority of the camera view is occluded by multiple dynamic objects over a long period of time. Most dynamic SLAM approaches either remove dynamic objects as outliers when they account for a minor proportion of the visual input, or detect dynamic objects using semantic segmentation before camera tracking. Therefore, dynamic objects that cause large occlusions are difficult to detect without prior information. The remaining visual information from the static background is also not enough to support localisation when large occlusion lasts for a long period. To overcome these problems, our framework presents a robust visual-inertial bundle adjustment that simultaneously tracks camera, estimates cluster-wise dense segmentation of dynamic objects and maintains a static sparse map by combining dense and sparse features. The experiment results demonstrate that our method achieves promising localisation and object segmentation performance compared to other state-of-the-art methods in the scenario of long-term large occlusion.

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Section: B Proprioception-aided Slammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

Long¹,

Rauch²,

Ivan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning has been applied to SLAM to improve its accuracy and robustness Deep learning has also been applied to SLAM for the purpose of addressing challenges such as handling dynamic objects, improving real-time performance, and dealing with large-scale environments. The DynaVINS approach, as presented in the recent paper by Song et al [272], represents a promising application of deep learning to enhance the robustness of visual-inertial SLAM in dynamic environments. This approach enables the system to continue building and updating the map even when the environment undergoes changes, thereby improving its overall robustness.…”

Section: Simultaneous Localization and Mapping (Slam)mentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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“…In the VINS-Mono method, visual-inertial odometry is obtained by fusing pre-integrated IMU measurements and feature observations. It is a state-ofthe-art visual-inertial odometry algorithm that has gained extensive attention worldwide, and several variants based on VINS-Mono have been proposed [39,40]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [39], a novel visual–inertial SLAM framework, called DynaVINS, is proposed. The method is robust against both dynamic objects and temporarily static objects, leading to good performance compared with other methods.…”

Section: Introductionmentioning

confidence: 99%

Sampling visual SLAM with a wide‐angle camera for legged mobile robots

Fan

Huang

Yang

et al. 2022

IET Cyber-Syst and Robotics

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Precise localisation and navigation are the two most important tasks for mobile robots. Visual simultaneous localisation and mapping (VSLAM) is useful in localisation systems of mobile robots. The wide-angle camera has a broad field of vision and more abundant information on images, so it is widely used in mobile robots, including legged robots. However, wide-angle cameras are more complicated than ordinary cameras in the design of visual localisation systems, and higher requirements and challenges are put forward for VSLAM technologies based on wide-angle cameras. In order to resolve the problem of distortion in wide-angle images and improve the accuracy of localisation, a sampling VSLAM based on a wide-angle camera model for legged mobile robots is proposed. For the predictability of the periodic motion of a legged robot, in the method, the images are sampled periodically, image blocks with clear texture are selected and the image details are enhanced to extract the feature points on the image. Then, the feature points of the blocks are extracted and by using the feature points of the blocks in the images, the feature points on the images are extracted. Finally, the points on the incident light through the normalised plane are selected as the template points; the relationship between the template points and the images is established through the wide-angle camera model, and the pixel coordinates of the template points in the images and the descriptors are calculated. Moreover, many experiments are conducted on the TUM datasets with a quadruped robot . The experimental results show that the trajectory error and translation error measured by the proposed method are reduced compared with the VINS-MONO, ORB-SLAM3 and Periodic SLAM systems.

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DynaVINS: A Visual-Inertial SLAM for Dynamic Environments

Cited by 64 publications

References 28 publications

RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Sampling visual SLAM with a wide‐angle camera for legged mobile robots

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