DV-LOAM: Direct Visual LiDAR Odometry and Mapping

Wang, Wei; Liu, Jun; Chenjie, Wang; Luo, Bin; Zhang, Cheng

doi:10.3390/rs13163340

Cited by 42 publications

(21 citation statements)

References 69 publications

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“…Main applications of RiWNet are dynamic Visual SLAM or visual-LiDAR fusion odometry/SLAM as well as 3D dense mapping. Here we show the effectiveness of RiWNet, adding RiWNet as a processing module to segment moving objects for keyframes in our previous work called DV-LOAM [62] of visual-LiDAR fusion SLAM. In DV-LOAM, because the relative transformation of the camera and laser is known, just using the image of the moving object segmentation result can handle the point cloud of the entire Visual LiDAR fusion SLAM.…”

Section: G Applicationsmentioning

confidence: 94%

RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions

Wang¹,

Li²,

Luo³

et al. 2021

Preprint

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Segmenting each moving object instance in a scene is essential for many applications. But like many other computer vision tasks, this task performs well in optimal weather, but then adverse weather tends to fail. To be robust in weather conditions, the usual way is to train network in data of given weather pattern or to fuse multiple sensors. We focus on a new possibility, that is, to improve its resilience to weather interference through the network's structural design. First, we propose a novel FPN structure called RiWFPN with a progressive topdown interaction and attention refinement module. RiWFPN can directly replace other FPN structures to improve the robustness of the network in non-optimal weather conditions. Then we extend SOLOV2 to capture temporal information in video to learn motion information, and propose a moving object instance segmentation network with RiWFPN called RiWNet. Finally, in order to verify the effect of moving instance segmentation in different weather disturbances, we propose a VKTTI-moving dataset which is a moving instance segmentation dataset based on the VKTTI dataset, taking into account different weather scenes such as rain, fog, sunset, morning as well as overcast. The experiment proves how RiWFPN improves the network's resilience to adverse weather effects compared to other FPN structures. We compare RiWNet to several other state-of-theart methods in some challenging datasets, and RiWNet shows better performance especially under adverse weather conditions.

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Section: G Applicationsmentioning

confidence: 94%

RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions

Wang¹,

Li²,

Luo³

et al. 2021

Preprint

Self Cite

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show abstract

“…In the context of odometry, one can classify how data is used to produce the system's output. For example, LiDAR measurements can complement the imagery, while estimating ego-motion with visual odometry or vice versa; or the two types of odometry can operate separately and fuse at a higher abstraction level of the system's framework [61]. The systems that follow the first approach are usually denoted as tightly-coupled, whereas the others are called loosely-coupled [62].…”

Section: A Data Fusion Strategiesmentioning

confidence: 99%

“…The work presented by W. Wang et al [61], DV-LOAM, is an example of how to combine LiDAR and camera data at various levels for improved ego-motion detection. DV-LOAM is composed of a front-end and a back-end part.…”

Section: B State-of-the-art Techniquesmentioning

confidence: 99%

A Practical Survey on Visual Odometry for Autonomous Driving in Challenging Scenarios and Conditions

et al. 2022

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The expansion of autonomous driving operations requires the research and development of accurate and reliable self-localization approaches. These include visual odometry methods, in which accuracy is potentially superior to GNSS-based traditional techniques while also working in signal-denied areas. This paper presents an in-depth review of state-of-the-art methods in visual and point cloud odometry, along with a direct performance comparison of some of these techniques in the autonomous driving context. The evaluated methods include camera, LiDAR, and multi-modal approaches, featuring knowledge and learning-based algorithms. This set was subject to a series of tests on road driving public datasets, from which the performance of these techniques is benchmarked and quantitatively compared. Furthermore, we closely discuss their effectiveness against challenging conditions such as pronounced lighting variations, open spaces, and the presence of dynamic objects in the scene, grouped by categories. The research addresses and corroborates some of the most prominent limitations of state-of-the-art techniques for visual odometry based on 2D and 3D sensors and points out the stagnation, in terms of performance, of the most recent advances in this area, especially in complex environments. We also examine how multi-modal architectures can circumvent these weaknesses and how the current advances in AI constitute a way to overcome the current stagnation, indexing some opportunities for future research.

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“…It makes up for the lack of loopback detection in DSO and uses the feature-based bag of words [7] to reliably detect loopbacks, reducing the cumulative error of the system. To reduce the computational complexity while obtaining accurate results, a typical SLAM solution called LOAM (Lidar Odometry and Mapping in Real-time) and its variants are proposed to achieve low-drift and real-time pose and mapping estimation by performing point-to-line and point-to-plane matching [8][9][10][11][12]. DV-LOAM [12] is a direct vision LiDAR fusion SLAM framework.…”

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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DV-LOAM: Direct Visual LiDAR Odometry and Mapping

Cited by 42 publications

References 69 publications

RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions

RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions

A Practical Survey on Visual Odometry for Autonomous Driving in Challenging Scenarios and Conditions

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

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