Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features

Lim, Hyunjun; Kim, Yeeun; Jung, Kwangik; Hu, Sumin; Myung, Hyun

doi:10.1109/icra48506.2021.9560911

Cited by 34 publications

(6 citation statements)

References 32 publications

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“…The feature point method extracts up to 1200 SuperPoint [ 46 ] feature points per frame and uses the SuperGLUE [ 47 ] model for matching. The AG3line method is used to extract line features, as explained in [ 27 ], while prediction and fusion are achieved using the optical flow method proposed in [ 37 ]. The entire process is illustrated in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

Zhou,

Han,

Nie

et al. 2024

Sensors

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Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR–camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.

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Section: Methodsmentioning

confidence: 99%

“…However, these methods typically require substantial computational resources. Lim et al [ 37 ] proposed a method that utilizes optical flow to obtain predicted lines and perform fusion. This method enables fast and effective matching and removal of duplicates, resulting in significant computational savings.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

Zhou,

Han,

Nie

et al. 2024

Sensors

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“…A degeneracy avoidance technique for monocular point- and line-based VSLAM systems was introduced by Lim et al [ 73 ]. Another contribution of their work is introducing an Optical Flow-based line tracking module that extracts line characteristics, filters out the short lines in each frame, and matches the previously identified ones.…”

Section: Vslam State-of-the-artsmentioning

confidence: 99%

Visual SLAM: What Are the Current Trends and What to Expect?

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

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In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them.

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“…In recent years, Simultaneous Localization and Mapping (SLAM) has been studied due to its numerous applications on various mobile platforms such as autonomous driving [1]- [4], unmanned aerial vehicles (UAVs) [5], unmanned ground vehicles (UGVs) [6], mobile phones [7], and so forth. Of course, SLAM can be performed by using various sensors, e.g.…”

Section: Introductionmentioning

confidence: 99%

PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

Seo¹,

Lim²,

Lee³

et al. 2022

Preprint

Self Cite

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Numerous researchers have conducted studies to achieve fast and robust ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In particular, groundoptimized LiDAR odometry usually employs ground segmentation as a preprocessing method. This is because most of the points in a 3D point cloud captured by a 3D LiDAR sensor on a terrestrial platform are from the ground. However, the effect of the performance of ground segmentation on LiDAR odometry is still not closely examined. In this paper, a robust ground-optimized LiDAR odometry framework is proposed to facilitate the study to check the effect of ground segmentation on LiDAR SLAM based on the state-of-the-art (SOTA) method. By using our proposed odometry framework, it is easy and straightforward to test whether ground segmentation algorithms help extract well-described features and thus improve SLAM performance. In addition, by leveraging the SOTA ground segmentation method called Patchwork, which shows robust ground segmentation even in complex and uneven urban environments with little performance perturbation, a novel ground-optimized LiDAR odometry is proposed, called PaGO-LOAM. The methods were tested using the KITTI odometry dataset. PaGO-LOAM shows robust and accurate performance compared with the baseline method. Our code is available at https://github.com/url-kaist/AlterGround-LeGO-LOAM

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Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features

Cited by 34 publications

References 32 publications

Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

Visual SLAM: What Are the Current Trends and What to Expect?

PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

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