首页|Researchers from Beijing Institute of Technology Report Re- cent Findings in Robotics and Automation (Lcpr: a Multi-scale Attention-based Lidar-camera Fusion Network for Place Recogni- tion)
Researchers from Beijing Institute of Technology Report Re- cent Findings in Robotics and Automation (Lcpr: a Multi-scale Attention-based Lidar-camera Fusion Network for Place Recogni- tion)
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NSTL
IEEE
Investigators publish new report on Robotics - Robotics and Automation. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Place recognition is one of the most crucial modules for autonomous vehicles to identify places that were previously visited in GPS-invalid environments. Sensor fusion is considered an effective method to overcome the weaknesses of individual sensors." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Beijing Institute of Technology, "In recent years, multimodal place recognition fusing information from multiple sensors has gathered increasing attention. However, most existing multimodal place recognition methods only use limited field-of-view camera images, which leads to an imbalance between features from different modalities and limits the effectiveness of sensor fusion. In this letter, we present a novel neural network named LCPR for robust multimodal place recognition, which fuses LiDAR point clouds with multi-view RGB images to generate discriminative and yaw-rotation invariant representations of the environment. A multi-scale attention-based fusion module is proposed to fully exploit the panoramic views from different modalities of the environment and their correlations."
BeijingPeople's Republic of ChinaAsiaRobotics and Au- tomationRoboticsBeijing Institute of Technology
NETL
NSTL
IEEE
