Abstract
Autonomous agents such as cars, robots and drones need to precisely localize themselves in diverse environments, including in GPS-denied indoor environments. One approach for precise localization is visual place recognition (VPR), which estimates the place of an image based on previously seen places. State-of-the-art VPR models require high amounts of memory, making them unwieldy for mobile deployment, while more compact models lack robustness and generalization capabilities. This work overcomes these limitations for robotics using a combination of event-based vision sensors and an event-based novel guided variational autoencoder (VAE). The encoder part of our model is based on a spiking neural network model which is compatible with power-efficient low latency neuromorphic hardware. The VAE successfully disentangles the visual features of 16 distinct places in our new indoor VPR dataset with a classification performance comparable to other state-of-the-art approaches while, showing robust performance also under various illumination conditions. When tested with novel visual inputs from unknown scenes, our model can distinguish between these places, which demonstrates a high generalization capability by learning the essential features of location. Our compact and robust guided VAE with generalization capabilities poses a promising model for visual place recognition that can significantly enhance mobile robot navigation in known and unknown indoor environments.
Abstract (translated)
自主代理（如汽车、机器人和无人机）需要在各种环境中精确定位自己，包括无GPS信号的室内环境。视觉位置识别（VPR）是一种精确定位的方法，它基于之前看到的位置来估计图像的位置。最先进的VPR模型需要大量内存，使其难以部署到移动设备中，而更紧凑的模型则缺乏鲁棒性和泛化能力。 这项研究通过结合事件驱动视觉传感器和一种新的事件驱动引导变分自编码器（VAE）的方法，解决了机器人技术中的这些限制。我们模型的编码部分基于脉冲神经网络模型，该模型与节能低延迟神经形态硬件兼容。我们的VAE在新的室内VPR数据集上成功分离了16个不同位置的视觉特征，并且其分类性能与其他最先进的方法相当，在各种光照条件下也表现出良好的鲁棒性。 当使用未知场景的新视觉输入进行测试时，我们的模型能够区分这些位置，这表明它具有很高的泛化能力，可以学习到地点的关键特征。我们紧凑、稳健并具备泛化的引导VAE为视觉位置识别提供了一个有前景的模型，能够在已知和未知的室内环境中显著增强移动机器人的导航能力。
URL
https://arxiv.org/abs/2601.09248
