A Novel Cooperative Localization Method Based on IMU and UWB

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).

VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

“…Copious studies have been done on visual-inertial SLAM along with various applications that have since been on a continuum to full maturity [ 9 , 10 , 11 ]. Until today, scholars are still endeavoring to bring it to perfection with novel ideas and approaches [ 12 , 13 , 14 , 15 , 16 ]. Incipient methods fuse inertial and visual measurements loosely where the two types of information are processed, filtered, and used for estimation all separately [ 17 ].…”

An IMU/ODM/UWB Joint Localization System Based on Modified Cubature Kalman Filtering

“…Hu et al [ 22 ] successfully reduced the computational load of the joint positioning system by decoupling the attitude and position information in their study using three systems (i.e., GPS, IMU, and Celestial Navigation System (CNS)) to navigate the spacecraft. Han et al [ 23 ] managed to decrease the number of UWB nodes from three to one by calibrating and compensating GPS signals using the UWB positioning as well as fusing the information of GPS and UWB positioning using an improved particle filter based on the ant colony optimization algorithm. Using IMU sensors to supplement UWB information, Li et al [ 24 ] successfully improved the ability of the UWB positioning system to deal with process noise by employing state enhancement, which was achieved by measuring Gaussian noise in the system speed with an accelerometer.…”

TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

“…Cooperative navigation has received extensive interest from the research field, like wireless sensor networks, mobile networks, and unmanned aerial vehicles (UAVs) [17][18][19]. Currently existing approaches for cooperative navigation are factor graphs and sum-product algorithms [20], semidefinite programming [21], particle filters [22], Kalman filters [23], and so on. In the cooperative navigation method, positioning users help each other to determine their locations.…”