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Article
Automation & Control Systems
Dong Kong et al.
Summary: This article presents a novel approach to global positioning based on LiDAR maps, which achieves high-frequency, accurate, and robust localization for autonomous vehicles or robots in complex environments by introducing a new observation model and neural network structure.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Fengkui Cao et al.
Summary: In this article, a novel end-to-end localizer is proposed for robust long-term localization in changing environments. A lightweight two-head place classification network is introduced to handle seasonal changes and local structure variations. An incremental learning strategy is also proposed for robust topological localization. Experimental results on three datasets validate the feasibility and performance of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Peng Yin et al.
Summary: This article proposes a new 3-D feature learning method to give robots a human-like place recognition ability. The method extracts viewpoint-invariant place features and uses a fast sequence matching mechanism to achieve robust 3-D place recognition. The proposed approach outperforms the existing state of the art, achieving above 95% average recall with only 18% inference time of PointNet-based methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Robotics
Lin Li et al.
Summary: LiDAR-based place recognition is a fundamental capability of robots, and this letter proposes a rotation invariant neural network structure for it. The network combines semantic and geometric features, design a rotation equivariant global descriptor, and uses a rotation invariant siamese neural network to predict descriptor pair similarity. The evaluations and tests on multiple datasets show that this approach can work stably in various scenarios and achieve state-of-the-art performance.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Xieyuanli Chen et al.
Summary: The paper introduces a modified Siamese network for estimating the similarity between pairs of LiDAR scans, which can be used for loop closing in SLAM and global localization in autonomous systems. The approach utilizes a deep neural network to exploit cues generated from LiDAR data, demonstrating superior performance and generalization in different environments. The method effectively detects loop closures and reliably localizes vehicles globally in urban environments using LiDAR data collected in various seasons.
Article
Robotics
Junyi Ma et al.
Summary: This article addresses the problem of place recognition based on 3D LiDAR scans and proposes a novel lightweight neural network approach. The author achieves fast execution using the range image representation of LiDAR sensors and designs a yaw-angle-invariant network architecture to improve place recognition performance. Experimental results demonstrate that the method performs well across different environments.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Proceedings Paper
Automation & Control Systems
Kavisha Vidanapathirana et al.
Summary: This paper introduces a local consistency loss to guide the network in learning consistent local features for improved 3D place recognition performance. Experimental results show that the method achieves state-of-the-art performance on two large-scale public benchmarks.
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022)
(2022)
Article
Robotics
Sourav Garg et al.
Summary: Visual Place Recognition (VPR) involves matching current visual imagery from a camera to images in a reference map, with recent advances focusing on more powerful visual features and iterative matching processes. This study introduces a hybrid system using short learnt sequential descriptors and single image learnt descriptors to generate high performance initial match hypotheses, outperforming existing methods on benchmark datasets.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Lun Luo et al.
Summary: This research introduces a Lidar-based framework called BVMatch for place recognition in large-scale environments, which estimates 2D relative poses by discretizing the ground area into grids and projecting them to bird's-eye view images. By utilizing Log-Gabor filters and maximum index maps, a new descriptor BVFT is proposed that is insensitive to rotation and intensity variations, unifying place recognition and pose estimation tasks in the BVMatch framework. Experiments conducted on three large-scale datasets demonstrate that BVMatch outperforms state-of-the-art methods in both recall rate of place recognition and pose estimation accuracy.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Fengkui Cao et al.
Summary: This article introduces a novel LiDAR-based place recognition system that achieves long-term robust localization under various perceptual conditions by utilizing a compact image model and the context of scenes. Experimental results demonstrate that the proposed system outperforms other methods in challenging scenarios.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Robotics
Peng Yin et al.
Summary: The letter introduces FusionVLAD, a fusion-based network for real-time 3D place recognition, which encodes a multiview representation of sparse 3D point clouds. It consists of two parallel branches for orientation-invariant and translation-insensitive feature extraction, with a parallel fusion module to enhance the combination of region-wise feature connection between the two branches. Experiments show that FusionVLAD outperforms state-of-the-art methods in terms of accuracy and efficiency.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Le Hui et al.
Summary: This paper proposes a Pyramid Point Cloud Transformer Network (PPT-Net) to learn discriminative global descriptors from point clouds for efficient retrieval. By developing a pyramid point transformer module and a pyramid VLAD module, the method extracts discriminative local features and aggregates multi-scale feature maps to achieve state-of-the-art results in point cloud based place recognition task.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Hao Dong et al.
Summary: This paper presents a novel pole extraction approach that can run online with low computational demands and outperforms other state-of-the-art methods on different datasets. The method performs computations directly on range images generated from 3D LiDAR scans, enabling fast pole extraction for each scan.
10TH EUROPEAN CONFERENCE ON MOBILE ROBOTS (ECMR 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Maneekwan Toyungyernsub et al.
Summary: The study introduces a dual-prong neural network architecture to predict the spatiotemporal evolution of occupancy state, achieving clearer predictions over longer time horizons.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Zhicheng Zhou et al.
Summary: This paper introduces a novel approach named NDT-Transformer for real-time and large-scale place recognition using 3D point clouds. By utilizing the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with geometrical and contextual information, achieving significant improvements in place recognition performance.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Xieyuanli Chen et al.
Summary: In this paper, range images from 3D LiDAR scans are utilized for accurate localization in large-scale outdoor environments. The proposed observation model integrated into a Monte Carlo localization framework achieves better performance and generalization ability in tracking vehicle pose online at the LiDAR sensor frame rate across different environments.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Stephen Hausler et al.
Summary: Visual Place Recognition is a challenging task that requires dealing with appearance and viewpoint changes in a changing world. Patch-NetVLAD introduces a novel approach that combines local and global descriptor methods to achieve superior performance and computational efficiency, making it suitable for enhancing both stand-alone place recognition capabilities and SLAM systems.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jacek Komorowski Warsaw
Summary: The paper introduces a learning-based method, MinkLoc3D, for computing discriminative 3D point cloud descriptors for place recognition. The method utilizes sparse voxelized point cloud representation and sparse 3D convolutions to achieve improved performance, outperforming current state-of-the-art methods in standard benchmarks.
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Han Wang et al.
2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2020)
Proceedings Paper
Automation & Control Systems
Giseop Kim et al.
2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2020)
Proceedings Paper
Automation & Control Systems
Peng Yin et al.
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2020)
Article
Computer Science, Artificial Intelligence
Filip Radenovic et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2019)
Article
Robotics
Olga Vysotska et al.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhe Liu et al.
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhe Liu et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)
(2019)
Proceedings Paper
Automation & Control Systems
Peng Yin et al.
2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Mikaela Angelina Uy et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2018)
Article
Robotics
Nicholas Carlevaris-Bianco et al.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2016)
Article
Robotics
Stephanie Lowry et al.
IEEE TRANSACTIONS ON ROBOTICS
(2016)