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基于惯性测量单元和超宽带的新型协同定位方法。

A Novel Cooperative Localization Method Based on IMU and UWB.

机构信息

School of Automation, Beijing Institute of Technology, Beijing 100081, China.

The 95894 Unit, PLA, Beijing 102211, China.

出版信息

Sensors (Basel). 2020 Jan 14;20(2):467. doi: 10.3390/s20020467.

DOI:10.3390/s20020467
PMID:31947587
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7013986/
Abstract

In this paper, a range-based cooperative localization method is proposed for multiple platforms of various structures. The localization system of an independent platform might degrade or fail due to various reasons such as GPS signal-loss, inertial measurement unit (IMU) accumulative errors, or emergency reboot. It is a promising approach to solve this problem by using information from neighboring platforms, thus forming a cooperative localization network that can improve the navigational robustness of each platform. Typical ranging-based ultra-wideband (UWB) cooperative localization systems require at least three auxiliary nodes to estimate the pose of the target node, which is often hard to meet especially in outdoor environment. In this work, we propose a novel IMU/UWB-based cooperative localization solution, which requires a minimum number of auxiliary nodes that is down to 1. An Adaptive Ant Colony Optimization Particle Filter (AACOPF) algorithm is customized to integrate the dead reckoning (DR) system and auxiliary nodes information with no prior information required, resulting in accurate pose estimation, while to our knowledge the azimuth have not been estimated in cooperative localization for the insufficient observation of the system. We have given the condition when azimuth and localization are solvable by analysis and by experiment. The feasibility of the proposed approach is evaluated through two filed experiments: car-to-trolley and car-to-pedestrian cooperative localization. The comparison results also demonstrate that ACOPF-based integration is better than other filter-based methods such as Extended Kalman Filter (EKF) and traditional Particle Filter (PF).

摘要

本文提出了一种基于范围的多平台合作定位方法，该方法适用于各种结构的平台。由于 GPS 信号丢失、惯性测量单元 (IMU) 累积误差或紧急重启等各种原因，独立平台的定位系统可能会降级或失效。通过使用来自相邻平台的信息来解决这个问题是一种很有前途的方法，从而形成一个合作定位网络，可以提高每个平台的导航鲁棒性。典型的基于测距的超宽带 (UWB) 合作定位系统至少需要三个辅助节点来估计目标节点的位置，这在室外环境中通常很难满足。在这项工作中，我们提出了一种新颖的基于 IMU/UWB 的合作定位解决方案，该方案需要的辅助节点数量最少，降至 1 个。定制了一种自适应蚁群优化粒子滤波 (AACOPF) 算法，用于将推算 (DR) 系统和辅助节点信息集成到一起，而无需任何先验信息，从而实现精确的位置估计，而据我们所知，由于系统的观测不足，合作定位中还没有估计方位。我们通过分析和实验给出了方位和定位可解的条件。通过两项现场实验：车对车和车对行人合作定位，评估了所提出方法的可行性。比较结果还表明，基于 ACOPF 的集成比其他基于滤波器的方法（如扩展卡尔曼滤波 (EKF) 和传统粒子滤波 (PF)）更好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/9a4d04311866/sensors-20-00467-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/045a352300b1/sensors-20-00467-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/f493cb11d331/sensors-20-00467-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/294d8cd9b3d0/sensors-20-00467-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/238a3fdf9f94/sensors-20-00467-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/cc84c465e533/sensors-20-00467-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/181006b5e984/sensors-20-00467-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/28e6a701e448/sensors-20-00467-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/71857e51dd07/sensors-20-00467-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/9a4d04311866/sensors-20-00467-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/045a352300b1/sensors-20-00467-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/e09d0ece8800/sensors-20-00467-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/376407e40e41/sensors-20-00467-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/46159063965d/sensors-20-00467-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/96c358f896f9/sensors-20-00467-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/f493cb11d331/sensors-20-00467-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/294d8cd9b3d0/sensors-20-00467-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/238a3fdf9f94/sensors-20-00467-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/cc84c465e533/sensors-20-00467-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/181006b5e984/sensors-20-00467-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/28e6a701e448/sensors-20-00467-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/71857e51dd07/sensors-20-00467-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf33/7013986/9a4d04311866/sensors-20-00467-g013.jpg

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