Scale Estimation and Correction of the Monocular Simultaneous Localization and Mapping (SLAM) Based on Fusion of 1D Laser Range Finder and Vision Data

Zhang, Zhuang; Zhao, Rujin; Liu, Enhai; Yan, Kun; Ma, Yuebo

doi:10.3390/s18061948

Cited by 43 publications

(31 citation statements)

References 41 publications

(44 reference statements)

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“…Zhang et al [70] proposed a monocular SLAM associated with a 1D laser range finder. As monocular SLAM often suffers from scale drift, this solution gave an efficient drift correction for a very low hardware cost.…”

Section: Improved Visual Slammentioning

confidence: 99%

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

Debeunne

Vivet

2020

Sensors

273

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: Improved Visual Slammentioning

confidence: 99%

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

Debeunne

Vivet

2020

Sensors

273

114

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“…In recent years, based on the works of LiDAR-SLAM and Visual-SLAM, some researchers have started to carry out the research of integrating such two main sensors [21][22][23][24][25]. In [21], the authors applied a visual odometer to provide initial values for two-dimensional laser Iterative Closets Point (ICP) on a small UAV, and achieved good results in real-time and accuracy.…”

Section: Multi-sensor Fusionmentioning

confidence: 99%

“…[24] presents a localization method based in cooperation between aerial and ground robots in an indoor environment, a 2.5D elevation map is built by RGB-D sensor and 2D LiDAR attached on UAV. [25] provides a scale estimation and drift correction method by combining mono laser range finder and camera for mono-SLAM. In [26], a visual SLAM system that combines images acquired from a camera and sparse depth information obtained from 3D LiDAR is proposed, by using the direct method.…”

Section: Multi-sensor Fusionmentioning

confidence: 99%

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

et al. 2019

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The method of simultaneous localization and mapping (SLAM) using a light detection and ranging (LiDAR) sensor is commonly adopted for robot navigation. However, consumer robots are price sensitive and often have to use low-cost sensors. Due to the poor performance of a low-cost LiDAR, error accumulates rapidly while SLAM, and it may cause a huge error for building a larger map. To cope with this problem, this paper proposes a new graph optimization-based SLAM framework through the combination of low-cost LiDAR sensor and vision sensor. In the SLAM framework, a new cost-function considering both scan and image data is proposed, and the Bag of Words (BoW) model with visual features is applied for loop close detection. A 2.5D map presenting both obstacles and vision features is also proposed, as well as a fast relocation method with the map. Experiments were taken on a service robot equipped with a 360° low-cost LiDAR and a front-view RGB-D camera in the real indoor scene. The results show that the proposed method has better performance than using LiDAR or camera only, while the relocation speed with our 2.5D map is much faster than with traditional grid map.

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“…When it comes to monocular SLAM, the absolute scale of the system is ambiguous [31]. Monocular SLAM can be fused with other facilities, including GNSS, IMU, and 1D laser range finder, to solve the scale ambiguity [32]- [34]. ORB-SLAM2 and VINSmono are representative algorithms of monocular SLAM [35], [36].…”

Section: Introductionmentioning

confidence: 99%

A Continuous Positioning Algorithm Based on RTK and VI-SLAM With Smartphones

et al. 2020

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The navigation technology has developed rapidly and immensely over the past few decades. Among the multiple navigation technologies, the representative and promising techniques are Real-time Kinematic (RTK) technique and Simultaneous Localization and Mapping (SLAM). RTK can provide realtime positioning results with high accuracy, while SLAM can not only locate the user but also construct a map of the new ambient. The first smartphone, the Xiaomi MI 8, equipped with the dual-frequency Global Navigation Satellite System (GNSS), hit the market in May 2018, providing valid carrier-phase measurements for RTK owe to the developer option of "Force full GNSS measurements." Nevertheless, RTK underperforms in urban areas as the buildings and trees can block the satellite signals. RTK cannot even provide positioning results when the GNSS outage happens. However, SLAM can effectively make up the drawbacks of RTK as it utilizes no more information than the images. SLAM can also be combined with the Inertial Measurement Unit (IMU) called Visual-Inertial SLAM (VI-SLAM,) with the improvement of accuracy and robustness. Therefore, our study mainly aims to confirm the feasibility of continuous positioning based on RTK and VI-SLAM with the Xiaomi MI 8. An application is developed to execute the functions, including logging images, measurements of IMU, and GPS measurements in Receiver INdependent EXchange (RINEX) format with the Xiaomi MI 8. The performances of RTK with and without the assistance of VI-SLAM are assessed respectively in the urban area. The experimental results demonstrate that the combination of RTK and VI-SLAM based on smartphones can effectively provide continuous positioning results. We believe our application will facilitate research and development in relation to positioning algorithms. Readers have access to this application at https://github.com/Nronaldo/CIGRLogger.

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Scale Estimation and Correction of the Monocular Simultaneous Localization and Mapping (SLAM) Based on Fusion of 1D Laser Range Finder and Vision Data

Cited by 43 publications

References 41 publications

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

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

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

A Continuous Positioning Algorithm Based on RTK and VI-SLAM With Smartphones

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