G2P-SLAM: Generalized RGB-D SLAM Framework for Mobile Robots in Low-Dynamic Environments

Song, Seungwon; Lim, Hyungtae; Jung, Sungwook; Myung, Hyun

doi:10.1109/access.2022.3151133

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…One of the solutions to this problem is to employ a wheel encoder. G2P-SLAM [19] rejected loop closure matching results with a high Mahalanobis distance from the estimated pose by the wheel odometry, which is invariant to the effect of dynamic and temporarily static objects. Despite the advantages of wheel encoder, these methods are highly dependent on the wheel encoder, limiting their own applicability.…”

Section: B Dynamic Objects Rejection In Visual and VI Slammentioning

confidence: 99%

DynaVINS: A Visual-Inertial SLAM for Dynamic Environments

Song

Lim

Lee

et al. 2022

IEEE Robot. Autom. Lett.

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Visual inertial odometry and SLAM algorithms are widely used in various fields, such as service robots, drones, and autonomous vehicles. Most of the SLAM algorithms are based on assumption that landmarks are static. However, in the real-world, various dynamic objects exist, and they degrade the pose estimation accuracy. In addition, temporarily static objects, which are static during observation but move when they are out of sight, trigger false positive loop closings. To overcome these problems, we propose a novel visual-inertial SLAM framework, called DynaVINS, which is robust against both dynamic objects and temporarily static objects. In our framework, we first present a robust bundle adjustment that could reject the features from dynamic objects by leveraging pose priors estimated by the IMU preintegration. Then, a keyframe grouping and a multi-hypothesis-based constraints grouping methods are proposed to reduce the effect of temporarily static objects in the loop closing. Subsequently, we evaluated our method in a public dataset that contains numerous dynamic objects. Finally, the experimental results corroborate that our DynaVINS has promising performance compared with other state-of-the-art methods by successfully rejecting the effect of dynamic and temporarily static objects. Our code is available at https://github.com/url-kaist/dynaVINS.

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Section: B Dynamic Objects Rejection In Visual and VI Slammentioning

confidence: 99%

DynaVINS: A Visual-Inertial SLAM for Dynamic Environments

Song

Lim

Lee

et al. 2022

IEEE Robot. Autom. Lett.

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“…A construction site continuously changes over time and on a large scale, and AI must be cognizant of up-to-date spatial information. Recently, simultaneous localization and mapping (SLAM) based on cameras [5][6][7] or light detection and ranging (LiDAR) systems [8,9] have been introduced to help robots localize themselves and recognize spaces even in unseen or unknown environments. When these localization and mapping Fig.…”

Section: Preliminariesmentioning

confidence: 99%

Low-cost Thermal Mapping for Concrete Heat Monitoring

Lee¹,

Cho²,

Myung³

2022

Proceedings of the 1st Future of Construction Workshop at the International Conference on Robotics and Automation (ICRA 2022)

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Robotics has been widely applied in smart construction for generating the digital twin or for autonomous inspection of construction sites. For example, for thermal inspection during concrete curing, continual monitoring of the concrete temperature is required to ensure concrete strength and to avoid cracks. However, buildings are typically too large to be monitored by installing fixed thermal cameras, and postprocessing is required to compute the accumulated heat of each measurement point. Thus, by using an autonomous monitoring system with the capability of long-term thermal mapping at a large construction site, both cost-effectiveness and a precise safety margin of the curing period estimation can be acquired. Therefore, this study proposes a low-cost thermal mapping system consisting of a 2D range scanner attached to a consumerlevel inertial measurement unit and a thermal camera for automated heat monitoring in construction using mobile robots.

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“…Of course, SLAM can be performed by using various sensors, e.g. RGB or RGB-D cameras [8], radar sensors, ultrasonic sensors. Some researchers have employed a 3D light detection and ranging (LiDAR) sensor to achieve a precise 3D point cloud map [9], [10].…”

Section: Introductionmentioning

confidence: 99%

PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

Seo¹,

Lim²,

Lee³

et al. 2022

Preprint

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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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G2P-SLAM: Generalized RGB-D SLAM Framework for Mobile Robots in Low-Dynamic Environments

Cited by 11 publications

References 28 publications

DynaVINS: A Visual-Inertial SLAM for Dynamic Environments

DynaVINS: A Visual-Inertial SLAM for Dynamic Environments

Low-cost Thermal Mapping for Concrete Heat Monitoring

PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

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