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Article
Robotics
Wei Xu et al.
Summary: This article presents FAST-LIO2, a fast, robust, and versatile LiDAR-inertial odometry framework. It introduces two key novelties, the direct registration of raw points to the map and the use of an incremental k-dimensional tree data structure for map maintenance. FAST-LIO2 achieves superior performance compared to existing methods and supports various LiDAR configurations and platforms.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Automation & Control Systems
Zheng Liu et al.
Summary: This article introduces a robotic lidar sensor based on incommensurable scanning to address the manufacturing difficulty of traditional lidars, with unique features and advantages for robotic applications.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Robotics
Chanoh Park et al.
Summary: The article introduces a novel map-centric SLAM framework, ElasticLiDAR++, which overcomes the challenges of multimodal sensor fusion and LiDAR motion distortion. Using a local continuous-time trajectory representation, the method achieves nonredundant yet dense mapping through a surface resolution preserving matching algorithm and surfel fusion model.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Robotics
Chunge Bai et al.
Summary: This letter presents an incremental voxel-based lidar-inertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. By using iVox as the point cloud spatial data structure, the method achieves high tracking speed without the need for complicated tree-based structures or strict k-nearest neighbor queries.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Chongjian Yuan et al.
Summary: This study proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The method utilizes voxel maps to probabilistically represent the environment and accurately register new LiDAR scans, achieving high accuracy and efficiency compared to other state-of-the-art methods.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Kailai Li et al.
Summary: A novel tightly-coupled LiDAR-inertial odometry and mapping scheme is proposed for both solid-state and mechanical LiDARs. This scheme utilizes a feature-based lightweight LiDAR odometry at the frontend and a hierarchical keyframe-based sliding window optimization at the backend to directly fuse IMU and LiDAR measurements, with a novel feature extraction method for the new solid-state LiDAR Livox Horizon's irregular scan pattern.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Chongjian Yuan et al.
Summary: The study introduces a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. It achieves high pixel-level accuracy by aligning natural edge features in the two sensors without the need for checkerboards. The method shows high robustness, accuracy, and consistency in various indoor and outdoor scenes, promoting research and application of LiDAR and camera fusion.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Han Wang et al.
Summary: This study presents a new SLAM framework for solid-state LiDAR sensors, which involves feature extraction, odometry estimation, and probability map building, providing a robust and efficient solution for localization and mapping in small scale robots.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
David Wisth et al.
Summary: The system presented is an efficient multi-sensor odometry system that optimizes visual, lidar, and inertial information in real-time. It utilizes a new method to extract 3D line and planar primitives from lidar point clouds and overcomes the suboptimality of typical frame-to-frame tracking methods. Through passive synchronization of lidar and camera frames, true integration of lidar features with standard visual features and IMU is achieved.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Zheng Liu et al.
Summary: The letter introduces a local Bundle Adjustment (BA) method for lidar SLAM, which effectively reduces drift and allows large-scale dense plane and edge features to be used. To speed up the optimization process, the method also introduces a novel adaptive voxelization method.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Wei Xu et al.
Summary: This study introduces a computationally efficient and robust LiDAR-inertial odometry framework for reliable navigation in fast-motion, noisy, or cluttered environments. By presenting a new formula for computing the Kalman gain, the computation load is reduced in the presence of a large number of measurements. The proposed method has been tested in various indoor and outdoor environments, showing reliable real-time navigation results.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Proceedings Paper
Automation & Control Systems
Masashi Yokozuka et al.
Summary: This paper proposes a 3D light detection and ranging SLAM method that significantly reduces the number of points used for point cloud registration using a novel ICP metric, and introduces symmetric KL-divergence to address the accuracy issue caused by reducing the number of points. Experiment results show high computational efficiency, better performance than other methods, and similar accuracy to the state-of-the-art SLAM method.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Yue Pan et al.
Summary: MULLS is an efficient, low-drift, and versatile 3D LiDAR SLAM system that uses feature point extraction and multi-metric linear least square algorithm for frame-to-submap registration to keep the map updated and reduce error from dead reckoning through hierarchical pose graph optimization.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Jan Quenzel et al.
Summary: The proposed method combines B-Spline trajectory representation and GMM for real-time 6D LiDAR odometry, utilizing sparse voxel grids and permutohedral lattices for fast access to map surfels. Experimental evaluations demonstrate the performance of the approach on multiple datasets and real-robot experiments.
2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2021)
Article
Engineering, Civil
Radhika Ravi et al.
TRANSPORTATION RESEARCH RECORD
(2020)
Article
Automation & Control Systems
David Droeschel et al.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Chanoh Park et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017)
(2017)
Article
Computer Science, Information Systems
Jeorg Stueckler et al.
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2014)
Article
Robotics
Martin Magnusson et al.
JOURNAL OF FIELD ROBOTICS
(2007)
Article
Computer Science, Artificial Intelligence
GC Sharp et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2002)
