WHU-Helmet: A Helmet-Based Multisensor SLAM Dataset for the Evaluation of Real-Time 3-D Mapping in Large-Scale GNSS-Denied Environments

Li, Jianping; Wu, Wenbo; Yang, Bisheng; Zou, Xianghong; Yang, Yuhui; Zhao, Xin; Dong, Zhen

doi:10.1109/tgrs.2023.3275307

Cited by 12 publications

(11 citation statements)

References 68 publications

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“…Several sequences (Carlevaris-Bianco et al, 2016;Geiger et al, 2012Geiger et al, , 2013Liao et al, 2023;Pandey et al, 2011) TA B L E 7 (Continued) handheld devices (Ramezani et al, 2020). Giubilato et al (2022) and Li, Wu, et al (2023) additionally introduce a specialized helmet dataset encompassing indoor, urban, and forest environments, while Ros et al (2016) presents a synthetic dataset. These datasets encompass a range of LiDAR sensor densities, including 16line (Carlevaris-Bianco et al, 2016;Jeong et al, 2018;Lee et al, 2021), 32-line (Caesar et al, 2020;Chang et al, 2019;Pitropov et al, 2021), 64-line (Geiger et al, 2012(Geiger et al, , 2013Ramezani et al, 2020;Zhang et al, 2021), solid-state (Li, Wu, et al, 2023), and dual LiDARs (Chen et al, 2018;Jeong et al, 2019;Nguyen et al, 2022).…”

Section: Datasetsmentioning

confidence: 99%

3D LiDAR SLAM: A survey

Zhang,

Shi,

2024

The Photogrammetric Record

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Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM.

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Section: Datasetsmentioning

confidence: 99%

3D LiDAR SLAM: A survey

Zhang,

Shi,

2024

The Photogrammetric Record

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“…However, it is important to note that their dataset did not include RGB information, which restricts the potential applications of their dataset. Another recent study by Li et al [ 15 ] presented a new sensing kit that collected LiDAR-IMU datasets in multiple GNSS-denied scenarios, including a forest environment. Instead of using a backpack or a handheld design, the authors chose to develop a helmet that integrated the sensors, such as LiDAR, IMU, and GNSS, while storing the rest of the hardware in a backpack.…”

Section: Background and Related Workmentioning

confidence: 99%

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

Cristóvão,

Portugal,

Carvalho

et al. 2023

Sensors

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Forestry operations have become of great importance for a sustainable environment in the past few decades due to the increasing toll induced by rural abandonment and climate change. Robotics presents a promising solution to this problem; however, gathering the necessary data for developing and testing algorithms can be challenging. This work proposes a portable multi-sensor apparatus to collect relevant data generated by several onboard sensors. The system incorporates Laser Imaging, Detection and Ranging (LiDAR), two stereo depth cameras and a dedicated inertial measurement unit (IMU) to obtain environmental data, which are coupled with an Android app that extracts Global Navigation Satellite System (GNSS) information from a cell phone. Acquired data can then be used for a myriad of perception-based applications, such as localization and mapping, flammable material identification, traversability analysis, path planning and/or semantic segmentation toward (semi-)automated forestry actuation. The modular architecture proposed is built on Robot Operating System (ROS) and Docker to facilitate data collection and the upgradability of the system. We validate the apparatus’ effectiveness in collecting datasets and its flexibility by carrying out a case study for Simultaneous Localization and Mapping (SLAM) in a challenging woodland environment, thus allowing us to compare fundamentally different methods with the multimodal system proposed.

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“…Nevertheless, deep learning methods consume massive computational resources when processing light detection and ranging (LiDAR) point cloud directly, prompting these methods to downsampling the original point cloud prior to input into the network to alleviate this problem [4]. The matching of features is crucial for deep learning‐based point cloud registration [5]. However, computational resources limit the quantity of features used for matching in large‐scale point clouds, which affects the performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

MDU‐sampling: Multi‐domain uniform sampling method for large‐scale outdoor LiDAR point cloud registration

Ou,

Zheng,

Zheng

2024

Electronics Letters

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Sampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi‐domain uniform sampling method (MDU‐sampling) for large‐scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the features of neighbouring points. Notably, only points on the same ring in LiDAR data are considered as neighbouring points, eliminating the need for additional neighbouring point search and thereby speeding up processing rates. Experimental results demonstrate that the approach enhances accuracy and robustness compared with benchmarks.

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WHU-Helmet: A Helmet-Based Multisensor SLAM Dataset for the Evaluation of Real-Time 3-D Mapping in Large-Scale GNSS-Denied Environments

Cited by 12 publications

References 68 publications

3D LiDAR SLAM: A survey

3D LiDAR SLAM: A survey

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

MDU‐sampling: Multi‐domain uniform sampling method for large‐scale outdoor LiDAR point cloud registration

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