Ubiquitous positioning for pedestrian in adverse environment has served a long standing challenge. Despite dramatic progress made by Deep Learning, multi-sensor deep odometry systems yet pose a high computational cost and suffer from cumulative drifting errors over time. Thanks to the increasing computational power of edge devices, we propose a novel ubiquitous positioning solution by integrating state-of-the-art deep odometry models on edge with an EKF (Extended Kalman Filter)-LoRa backend. We carefully compare and select three sensor modalities, i.e., an Inertial Measurement Unit (IMU), a millimetre-wave (mmWave) radar, and a thermal infrared camera, and realise their deep odometry inference engines which runs in real-time. A pipeline of deploying deep odometry considering accuracy, complexity, and edge platform is proposed. We design a LoRa link for positional data backhaul and projecting aggregated positions of deep odometry into the global frame. We find that a simple EKF based fusion module is sufficient for generic positioning calibration with over 34% accuracy gains against any standalone deep odometry system. Extensive tests in different environments validate the efficiency and efficacy of our proposed positioning system.
翻译:尽管深层学习、多传感器或深电离测量系统取得了巨大进展,但计算成本仍然很高,并随时间推移出现累积的漂移错误。由于边缘装置的计算能力不断提高,我们提议采用一种新的无处不在的定位解决方案,将边缘最先进的深深视测量模型与EKF(Expendive Kalman Filter)- LoRa后端结合起来。我们仔细比较并选择了三种传感器模式,即惰性测量单元(IMU)、毫米波雷达(mmWave)和热红外摄像头,并实现了实时运行的深奥光度推断引擎。我们提议了一种考虑到准确性、复杂性和边缘平台的深奥光度测量管道。我们设计了定位数据背部的LoRa链接,并将深色测量的汇总位置投射到全球框架中。我们发现,一个简单的基于EKF组合模块,足以在全方位定位校准定位时使用超过34%的精确度的系统,在任何远方位定位定位系统上进行超过34%的精确度测试。