相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Editorial Material
Computer Science, Hardware & Architecture
Frank Dellaert
COMMUNICATIONS OF THE ACM
(2022)
Article
Robotics
Michal Adamkiewicz et al.
Summary: The article introduces the use of NeRF for representing 3D scenes, and proposes an algorithm for robot navigation using an RGB camera, including trajectory optimization and pose estimation methods. By combining the trajectory planner with the pose filter in an online replanning loop, a vision-based robot navigation pipeline is established.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Proceedings Paper
Automation & Control Systems
Nicky Zimmerman et al.
Summary: This paper addresses the problem of localizing in an indoor environment with changes and discrepancies between the map and the observed environment. By utilizing human-readable localization cues and integrating them into a Monte Carlo localization framework with a particle filter, a robust localization solution is provided for environments with structural changes and dynamics caused by humans.
2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zihan Zhu et al.
Summary: This paper presents NICE-SLAM, a dense SLAM system that incorporates multi-level local information through a hierarchical scene representation. It achieves detailed reconstruction on large indoor scenes and demonstrates competitive results in both mapping and tracking quality.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhixiang Min et al.
Summary: LASER is an image-based Monte Carlo Localization (MCL) framework that utilizes latent space rendering to present 2D pose hypotheses directly into a geometrically-structured latent space. By dynamically determining viewing ray features through a codebook scheme and applying metric learning, LASER achieves state-of-the-art performance on large-scale indoor localization datasets for both panorama and perspective image queries.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Konstantinos Rematas et al.
Summary: The goal of this work is to reconstruct 3D models and synthesize novel views using data captured by scanning platforms, with significant improvements achieved through the utilization of lidar data, addressing exposure variation, and leveraging image segmentations. The system outperforms traditional methods and recent neural representations in Street View data.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Kangle Deng et al.
Summary: DS-NeRF is a method that learns radiance fields using depth supervision, allowing for better image rendering with fewer training views and faster training. It leverages structure-from-motion (SFM) and sparse 3D points as depth supervision, demonstrating the advantages of depth as a cheap and easily understandable supervisory signal. It is also compatible with other types of depth supervision.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Edgar Sucar et al.
Summary: For the first time, a multilayer perceptron is used as the sole scene representation in a real-time SLAM system for a handheld RGB-D camera. The network is trained in live operation to build a dense, scene-specific implicit 3D model of occupancy and color, allowing for immediate tracking. The iMAP algorithm achieves real-time SLAM through continual training with dynamic information-guided pixel sampling for speed and efficient geometry representation.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jiaxin Li et al.
Summary: The proposed MINE in this paper combines novel view synthesis and depth estimation through dense 3D reconstruction from a single image, outperforming state-of-the-art methods in extensive experiments. The results show competitive performance in depth estimation without annotated depth supervision.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zike Yan et al.
Summary: This paper introduces a method called Continual Neural Mapping that enables continual learning of implicit scene representation directly from sequential observations, bridging the gap between batch-trained implicit neural representations and streaming data in robotics and vision communities. The proposed approach uses experience replay to approximate a continuous signed distance function from sequential depth images, showing that a single network can continually represent scene geometry over time without catastrophic forgetting, while maintaining a promising balance between accuracy and efficiency.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 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)
Article
Robotics
Jiaheng Zhao et al.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2020)
Article
Automation & Control Systems
Abdurrahman Yilmaz et al.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2019)
Proceedings Paper
Automation & Control Systems
Fan Yan et al.
2019 EUROPEAN CONFERENCE ON MOBILE ROBOTS (ECMR)
(2019)
Article
Chemistry, Analytical
Guillem Vallicrosa et al.
Article
Robotics
Fabio Ramos et al.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2016)
Article
Robotics
Simon T. O'Callaghan et al.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2012)
Article
Robotics
Giorgio Grisetti et al.
IEEE TRANSACTIONS ON ROBOTICS
(2007)
