CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System

Wang, Yahui; Cai, Shaojun; Li, Shijie; Liu, Yun; Guo, Yang-Yan; Li, Tao; Cheng, Ming–Ming

doi:10.1007/978-3-030-20876-9_3

Cited by 15 publications

(13 citation statements)

References 30 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used a panoramic annular lens with a FoV of 360 • ×(30 • ∼92 • ) and a global shutter camera for experiments. They confirmed the better performance of PA-SLAM with respect to ORB-SLAM2 [310] and a cube-map-based fisheye SLAM method [312] on real-world data. PAL-SLAM [169] designed a feature-based SLAM for panoramic annular lens with a FoV of 360 • ×(45 • ∼85 • ).…”

Section: Visual Localization Odometry and Slamsupporting

confidence: 55%

Review on Panoramic Imaging and Its Applications in Scene Understanding

Gao¹,

Yang²,

Sheng³

et al. 2022

Preprint

View full text Add to dashboard Cite

With the rapid development of high-speed communication and artificial intelligence technologies, human perception of real-world scenes is no longer limited to the use of small Field of View (FoV) and low-dimensional scene detection devices. Panoramic imaging emerges as the next generation of innovative intelligent instruments for environmental perception and measurement. However, while satisfying the need for large-FoV photographic imaging, panoramic imaging instruments are expected to have high resolution, no blind area, miniaturization, and multi-dimensional intelligent perception, and can be combined with artificial intelligence methods towards the next generation of intelligent instruments, enabling deeper understanding and more holistic perception of 360 • real-world surrounding environments. Fortunately, recent advances in freeform surfaces, thinplate optics, and metasurfaces provide innovative approaches to address human perception of the environment, offering promising ideas beyond conventional optical imaging. In this review, we begin with introducing the basic principles of panoramic imaging systems, and then describe the architectures, features, and functions of various panoramic imaging systems. Afterwards, we discuss in detail the broad application prospects and great design potential of freeform surfaces, thin-plate optics, and metasurfaces in panoramic imaging. We then provide a detailed analysis on how these techniques can help enhance the performance of panoramic imaging systems. We further offer a detailed analysis of applications of panoramic imaging in scene understanding for autonomous driving and robotics, spanning panoramic semantic image segmentation, panoramic depth estimation, panoramic visual localization, and so on. Finally, we cast a perspective on future potential and research directions for panoramic imaging instruments.

show abstract

Section: Visual Localization Odometry and Slamsupporting

confidence: 55%

Review on Panoramic Imaging and Its Applications in Scene Understanding

Gao¹,

Yang²,

Sheng³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For visual SLAM, we test ORB-SLAM3 [9], Cubemap-SLAM [8], Multicol-SLAM [11], and VINS-Mono [7]. The default setting of each method is used for evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…We tested Multicol-SLAM [11] with three fisheye cameras (two in the front, one on the left side), but it lost its track in almost every sequence. The reason might be that it tries to extract and match features directly from highly distorted images, which may easily cause false matches [8].…”

Section: Discussionmentioning

confidence: 99%

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

Yin¹,

Li²,

Li³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Global Navigation Satellite System (GNSS) receiver and a GNSS-IMU navigation system with real-time kinematic (RTK) signals. All those sensors were well-calibrated and synchronized, and their data were recorded simultaneously. The ground truth trajectories were obtained by the motion capture device, a laser 3D tracker, and an RTK receiver. The dataset comprises 36 sequences (about 1TB) captured in diverse scenarios including both indoor and outdoor environments. We evaluate state-of-the-art SLAM algorithms on M2DGR. Results show that existing solutions perform poorly in some scenarios. For the benefit of the research community, we make the dataset and tools public. The webpage of our project is https://github.com/SJTU-ViSYS/M2DGR.

show abstract

“…5(c). For example, [53] proposed reprojecting spherical images into cubic-map images for feature detection and implementing a SLAM (Simultaneous Localization and Mapping) system, termed Cube-mapSLAM.…”

Section: Image Matchingmentioning

confidence: 99%

3D reconstruction of spherical images: A review of techniques, applications, and prospects

Li¹,

Weng²,

You³

et al. 2023

Preprint

View full text Add to dashboard Cite

3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively used due to the capability of recording surrounding environments from one camera exposure. In contrast to perspective images with limited FOV (Field of View), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumergrade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction of spherical images. As a result, this research provides a thorough survey of the state-ofthe-art for 3D reconstruction of spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research.

show abstract

CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System

Cited by 15 publications

References 30 publications

Review on Panoramic Imaging and Its Applications in Scene Understanding

Review on Panoramic Imaging and Its Applications in Scene Understanding

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

3D reconstruction of spherical images: A review of techniques, applications, and prospects

Contact Info

Product

Resources

About