A New Approach to Train Convolutional Neural Networks for Real-Time 6-DOF Camera Relocalization

Esfahani, Mahdi Abolfazli; Wu, Keyu; Yuan, Shenghai; Wang, Han

doi:10.1109/icca.2018.8444299

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Recently, many place recognition methods have been proposed based on visual information to achieve global localization. [1][2][3][4][5] Camera can provide a lot of detailed information about the surrounding environment, which is useful for place recognition or global localization. However, for constantly changing industrial scenes or vision degraded scenes, which will have a fatal impact on the place recognition or global localization, vision-based localization methods usually fail.…”

Section: Introductionmentioning

confidence: 99%

High-precision and robust localization system for mobile robots in complex and large-scale indoor scenes

Wang

Li²,

Li³

et al. 2021

International Journal of Advanced Robotic Systems

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High-precision and robust localization is the key issue for long-term and autonomous navigation of mobile robots in industrial scenes. In this article, we propose a high-precision and robust localization system based on laser and artificial landmarks. The proposed localization system is mainly composed of three modules, namely scoring mechanism-based global localization module, laser and artificial landmark-based localization module, and relocalization trigger module. Global localization module processes the global map to obtain the map pyramid, thus improve the global localization speed and accuracy when robots are powered on or kidnapped. Laser and artificial landmark-based localization module is employed to achieve robust localization in highly dynamic scenes and high-precision localization in target areas. The relocalization trigger module is used to monitor the current localization quality in real time by matching the current laser scan with the global map and feeds it back to the global localization module to improve the robustness of the system. Experimental results show that our method can achieve robust robot localization and real-time detection of the current localization quality in indoor scenes and industrial environment. In the target area, the position error is less than 0.004 m and the angle error is less than 0.01 rad.

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