Abstract
The main objective of this paper is to address the mobile robot localization problem with Triplet Convolutional Neural Networks and test their robustness against changes of the lighting conditions. We have used omnidirectional images from real indoor environments captured in dynamic conditions that have been converted to panoramic format. Two approaches are proposed to address localization by means of triplet neural networks. First, hierarchical localization, which consists in estimating the robot position in two stages: a coarse localization, which involves a room retrieval task, and a fine localization is addressed by means of image retrieval in the previously selected room. Second, global localization, which consists in estimating the position of the robot inside the entire map in a unique step. Besides, an exhaustive study of the loss function influence on the network learning process has been made. The experimental section proves that triplet neural networks are an efficient and robust tool to address the localization of mobile robots in indoor environments, considering real operation conditions.
Abstract (translated)
本文的主要目标是使用三元卷积神经网络（Triplet Convolutional Neural Networks）解决移动机器人定位问题，并测试它们对照明条件变化的鲁棒性。我们使用从动态条件下捕获的现实室内环境中捕获的全方位图像，并将其转换为全景格式。我们提出了两种通过三元神经网络解决定位的方法。首先，是分层定位，其包括粗定位和细定位两个阶段：粗定位涉及房间检索任务，而细定位通过先前选定的房间的图像检索来解决。其次，是全局定位，它包括在一次性估计机器人在整个地图上的位置。此外，对网络学习过程中损失函数影响的全面研究已经进行了探讨。实验部分证明，三元神经网络是解决移动机器人室内环境定位的有效且鲁棒工具。考虑到实际操作条件。
URL
https://arxiv.org/abs/2404.14117
