Adaptive Keyframe Generation based LiDAR Inertial Odometry for Complex Underground Environments

Kim, Boseong; Jung, Choong–Hwan; Shim, David Hyunchul; Agha–mohammadi, Ali–akbar

doi:10.1109/icra48891.2023.10161207

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…This coupling can be directly applied to LiDAR‐only SLAM systems with little modification. Representative works are IMU‐aided LOAM, LeGO‐LOAM, MULLS, Adaptive Keyframe LIO (Kim et al., 2023) (Figure 10a), etc. Another more popular loose coupling approach is to perform state estimation based on LiDAR and IMU observations separately, and then fuse the LiDAR odometry (LO) and IMU odometry results using Kalman filtering (Kalman, 1960) or other technologies.…”

Section: Literature Reviewmentioning

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: Literature Reviewmentioning

confidence: 99%

3D LiDAR SLAM: A survey

Zhang,

Shi,

2024

The Photogrammetric Record

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An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

Kim,

Azhari,

Park

et al. 2024

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require lightweight but accurate state estimation algorithms for reliable exploration. In this paper, we propose a segmented map‐based exploration system based on light detection and ranging (LiDAR)‐based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using three‐dimensional (3D) dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by the proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real‐world environments and validated that the proposed system outperforms state‐of‐the‐art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments.

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Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging

Damigos,

Stathoulopoulos,

Koval

et al. 2024

J Intell Robot Syst

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Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.

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Adaptive Keyframe Generation based LiDAR Inertial Odometry for Complex Underground Environments

Cited by 8 publications

References 17 publications

3D LiDAR SLAM: A survey

3D LiDAR SLAM: A survey

An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging

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