计算机科学
计算机视觉
人工智能
同时定位和映射
奇异值分解
特征(语言学)
特征提取
机器人
移动机器人
语言学
哲学
作者
Jiajun Jiang,Xingxin Chen,Weichen Dai,Zelin Gao,Yu Zhang
出处
期刊:IEEE robotics & automation letters
[Institute of Electrical and Electronics Engineers]
日期:2022-10-01
卷期号:7 (4): 8767-8774
被引量:1
标识
DOI:10.1109/lra.2022.3185385
摘要
In recent years, longwave infrared (LWIR) cameras have become potential in visual simultaneous localization and mapping (SLAM) research since the delivered thermal images can provide information beyond the visible spectrum and are robust to environment illumination. However, due to modality differences, SLAM methods designed for visible cameras cannot be directly applied to thermal data. In this paper, we propose a thermal-inertial SLAM method for all-day autonomous systems. To overcome the challenge of the thermal data association, the proposed method represents several improvements, including singular-value-decomposition-based (SVD-based) image processing and ThermalRAFT tracking methods. Based on the characteristics of the thermal images, the SVD-based image processing method can exploit the fixed noise pattern of thermal images and enhance the image quality to improve the performance of subsequent steps, including thermal feature extraction and loop detection. To achieve real-time and robust feature tracking, we develop ThermalRAFT, an efficient optical flow network with iterative optimization. Moreover, the system introduces a bag-of-words-based loop detection method to maintain global consistency in long-term operation. The experimental results demonstrate that the proposed method can provide competitive performance in indoor and outdoor environments and is robust under challenging illumination conditions.
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