SC-LiDAR-SLAM: A Front-end Agnostic Versatile LiDAR SLAM System

Kim, Giseop; Yun, Seungsang; Kim, Jeongyun; Kim, Ayoung

doi:10.1109/iceic54506.2022.9748644

Cited by 21 publications

(6 citation statements)

References 38 publications

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“…The SC-LiDAR-SLAM open-source framework (Kim et al, 2022) was adopted for processing the collected LiDAR data. A-LOAM was used as a source of LiDAR odometry for Velodyne and FAST-LIO was selected for Livox.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Low-Cost Handheld Lidar-Based Slam Systems for Mapping Underground Tunnels

Trybała

Kasza

Wajs

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The use of mobile mapping technologies (MMT) has become increasingly popular across various applications such as forestry, cultural heritage, mining, and civil engineering. While Simultaneous Localization and Mapping (SLAM) algorithms have greatly improved in recent years with regards to accuracy, robustness, and cooperativity, it is important to understand the limitations and strengths of each metrological measurement method to ensure the provision of 3D data of appropriate quality for the selected application. In this study, we perform a comparative analysis of three LiDAR-based handheld mobile mapping systems with survey-grade reference point clouds in a challenging test area of a partially collapsed underground tunnel. We investigate various aspects of 3D data quality, including accuracy and completeness, and present an improved method for 3D data completeness assessment aimed at evaluating SLAM-derived point clouds. The results demonstrate unique and diverse strengths and shortcomings of the tested mapping systems, which provide valuable guidelines for selecting an appropriate system for subterranean applications.

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Section: Resultsmentioning

confidence: 99%

Comparison of Low-Cost Handheld Lidar-Based Slam Systems for Mapping Underground Tunnels

Trybała

Kasza

Wajs

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Inspired by [ 37 ], we integrated our IS-CAT model as a loop closure detection module in a 3D LiDAR SLAM system, particularly tested on the Sejong indoor-5F dataset, an indoor environment. Our approach began with the construction of the initial pose-graph using A-LOAM, which is a recognized LiDAR odometry method with only LiDAR data.…”

Section: Methodsmentioning

confidence: 99%

IS-CAT: Intensity–Spatial Cross-Attention Transformer for LiDAR-Based Place Recognition

Joo,

Kim

2024

Sensors

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LiDAR place recognition is a crucial component of autonomous navigation, essential for loop closure in simultaneous localization and mapping (SLAM) systems. Notably, while camera-based methods struggle in fluctuating environments, such as weather or light, LiDAR demonstrates robustness against such challenges. This study introduces the intensity and spatial cross-attention transformer, which is a novel approach that utilizes LiDAR to generate global descriptors by fusing spatial and intensity data for enhanced place recognition. The proposed model leveraged a cross attention to a concatenation mechanism to process and integrate multi-layered LiDAR projections. Consequently, the previously unexplored synergy between spatial and intensity data was addressed. We demonstrated the performance of IS-CAT through extensive validation on the NCLT dataset. Additionally, we performed indoor evaluations on our Sejong indoor-5F dataset and demonstrated successful application to a 3D LiDAR SLAM system. Our findings highlight descriptors that demonstrate superior performance in various environments. This performance enhancement is evident in both indoor and outdoor settings, underscoring the practical effectiveness and advancements of our approach.

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“…In [36], the intensity information over the local Lidar scan was introduced to build the intensity scan context (ISC). SC-LiDAR-SLAM was proposed in [37] as a complete SLAM system that integrated front-end place recognition with Scan Context++ to eliminate accumulated errors. These methods verify that both geometric and intensity information can construct descriptors to achieve loop detection effectively.…”

Section: Related Workmentioning

confidence: 99%

“…These methods verify that both geometric and intensity information can construct descriptors to achieve loop detection effectively. A semantic-based place recognition algorithm was proposed in [37], named Semantic Scan Context (SSC), which consisted of a two-step global iterative closest point and semantic-based descriptor.…”

Section: Related Workmentioning

confidence: 99%

Fast Lidar Inertial Odometry and Mapping for Mobile Robot SE(2) Navigation

Chen,

Sun

2023

Applied Sciences

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This paper presents a fast Lidar inertial odometry and mapping (F-LIOM) method for mobile robot navigation on flat terrain with high real-time pose estimation, map building, and place recognition. Existing works on Lidar inertial odometry have mostly parameterized the keyframe pose as SE(3) even when the robots moved on flat ground, which complicated the motion model and was not conducive to real-time non-linear optimization. In this paper, F-LIOM is shown to be cost-effective in terms of model complexity and computation efficiency for robot SE(2) navigation, as the motions in other degrees of freedom in 3D, including roll, pitch, and z, are considered to be noise terms that corrupt the pose estimation. For front-end place recognition, the smoothness information of the feature point cloud is introduced to construct a novel global descriptor that integrates geometry and environmental texture characteristics. Experiments under challenging scenarios, including self-collected datasets and public datasets, were conducted to validate the proposed method. The experimental results demonstrated that F-LIOM could achieve competitive real-time performance in terms of accuracy compared with state-of-the-art counterparts. Our solution has significant superiority and the potential to be deployed in limited-resource mobile robot systems.

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SC-LiDAR-SLAM: A Front-end Agnostic Versatile LiDAR SLAM System

Cited by 21 publications

References 38 publications

Comparison of Low-Cost Handheld Lidar-Based Slam Systems for Mapping Underground Tunnels

Comparison of Low-Cost Handheld Lidar-Based Slam Systems for Mapping Underground Tunnels

IS-CAT: Intensity–Spatial Cross-Attention Transformer for LiDAR-Based Place Recognition

Fast Lidar Inertial Odometry and Mapping for Mobile Robot SE(2) Navigation

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