Simultaneous Localization and Mapping (SLAM) has used RGB camera as a prior choice of sensor traditionally. However, in dark or low-light conditions, the image quality of RGB cameras deteriorates greatly, making reliable attitude estimation difficult. In contrast, infrared cameras are not affected by changes of ambient light, making them a viable sensing alternative. In this paper, We propose a calibration method for infrared camera and IMU to achieve higher pose estimation accuracy under the conditions of low light environment and heat treatment. Meanwhile, we propose a system called I2-SLAM based on MSCKF that is suitable for fusion positioning of infrared camera and IMU. We test the performance of I2-SLAM in daytime and night scenes on the vehicle-mounted data set, and compare it with other VIO systems. In result, the fusion of the infrared camera and IMU in the night scene can achieve high accuracy.


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    Titel :

    I2-SLAM: Fusing Infrared Camera and IMU for Simultaneous Localization and Mapping


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Hua, Tong (Autor:in) / Pei, Ling (Autor:in) / Li, Tao (Autor:in) / Wu, Qi (Autor:in) / Wang, Ruochen (Autor:in) / Yu, Wenxian (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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