Graph Optimization Approach to Range-based Localization

In this paper, we propose a general graph optimization based framework for localization, which can accommodate different types of measurements with varying measurement time intervals. Special emphasis will be on range-based localization. Range and trajectory smoothness constraints are constructed in a position graph, then the robot trajectory over a sliding window is estimated by a graph based optimization algorithm. Moreover, convergence analysis of the algorithm is provided, and the effects of the number of iterations and window size in the optimization on the localization accuracy are analyzed. Extensive experiments on quadcopter under a variety of scenarios verify the effectiveness of the proposed algorithm and demonstrate a much higher localization accuracy than the existing range-based localization methods, especially in the altitude direction

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an ineffcient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The First two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error

WALLSY: The UWB and SmartMesh IP enabled Wireless Ad-hoc Low-power Localization SYstem

This paper follows the implementation of a proofof-concept localization system for GNSS-denied environments. WALLSY (Wireless Ad-hoc Low-power Localization SYstem) is a portable and modular Ultra Wide-Band (UWB) and Smart Mesh IP (SMIP) hybrid. WALLSY uses UWB two way ranging (TWR) to measure distances, which are then sent via the lowpower SMIP backbone network to a central hub for calculating coordinates of tracked objects. The system is highly flexible and requires no external infrastructure or prior knowledge of the installation site. It uses a completely nomadic topology and delivers high localization accuracy with all modules being battery powered. It achieves this by using a custom time-slotting protocol which maximizes deep-sleep mode for UWB. Battery life can be further improved by activating inertial measurement unit (IMU) filtering. Visualization of tracked objects and system reconfiguration can be executed on-the-fly and are both accessible to end users through a simple graphical user interface (GUI). Results demonstrate that WALLSY can achieve more than ten times longer battery lifetime compared to competing solutions (localizing every 30 seconds). It provides 3D coordinates with an average spatial error of 60.5cm and an average standard deviation of 15cm. The system also provides support for up to 20 tags