A Simultaneous Localization and Mapping (SLAM) Framework for 2.5D Map Building Based on Low-Cost LiDAR and Vision Fusion

Jiang, Guolai; Yin, Lei; Jin, Shaokun; Tian, Chaoran; Ma, Xinbo; Ou, Yongsheng

doi:10.3390/app9102105

Cited by 57 publications

(44 citation statements)

References 29 publications

(41 reference statements)

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“…Maybe the most tight fusion currently available was proposed in [78], where a graph optimization was performed, using a specific cost function considering both laser and feature constraints. Here, both the laser data and image data could obtain the robot pose estimation.…”

Section: Concurrent Lidar-visual Slammentioning

confidence: 99%

A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

Debeunne

Vivet

2020

Sensors

293

114

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Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

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Section: Concurrent Lidar-visual Slammentioning

confidence: 99%

A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

Debeunne

Vivet

2020

Sensors

293

114

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“…Stereo visual odometry is used to track points and lines, and Gauss Newton optimization is then employed to estimate the motion of the camera by minimizing the projection errors of the corresponding features. In [59], observations of point features are combined with laser scans and used in a factor graph to estimate the pose of the robot. A new map representation combining both an occupancy grid map and point features was proposed.…”

Section: Multiple Feature Types To Aid Robustnessmentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

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“…The information necessary for autonomous navigation include the following systems: the navigation system, responsible for performing the movements; the control, for performing actions and corrections related to its navigation; and the sensory system, whose purpose is to analyze the internal states of the system and the environment, according to the orientation and speed. Thus, such systems recognize structures and create maps, which are obtained by comparing interval data with the estimation of the position in which it is located [32,33]. The technique to build a 2D map of the environment and estimate the position of a mobile robot simultaneously and interactively is called Simultaneous Localization and Mapping (SLAM).…”

Section: Slam Techniquementioning

confidence: 99%