Relative Position Estimation Between Two UWB Devices With IMUs

Abstract

For a team of robots to work collaboratively, it is crucial that each robot have the ability to determine the position of their neighbors, relative to themselves, in order to execute tasks autonomously. This letter presents an algorithm for determining the three-dimensional relative position between two mobile robots, each using nothing more than a single ultrawideband transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding window filter estimates the relative position at selected keypoints by combining the distance measurements with acceleration estimates, which each agent computes using an on-board attitude estimator. The algorithm is appropriate for real-time implementation, and has been tested in simulation and experiment, where it comfortably outperforms standard estimators. A positioning accuracy of less than 1 meter is achieved with inexpensive sensors. Index Terms-Localization; Range Sensing; Multi-Robot Systems I. INTRODUCTIONT HE ability to determine the relative position between two robots, or agents, is needed in many applications. For instance, multi-robot tasks such as collaborative exploration and mapping, search and rescue, and formation control, each require relative position information. Access to inter-robot distance measurements is becoming increasingly accessible due to technologies such as ultra-wideband radio (UWB). UWB in particular is attractive due to its low cost, low power, high accuracy, and ability to function in GPS-denied environments. Such advantages have even warranted the presence of UWB transceivers in smartphones and smartwatches, which could be used to provide position information of one user relative to another or, more generally, any other UWB transceiver. However, determining a full three-dimensional relative position estimate from a single distance measurement is impossible. There is an infinite set of relative positions that will produce the same distance measurement. Hence, to realize an observable relative positioning solution, more information is required.

Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System

“…This allows increased system scale, allowing more tags to be located within the region of interest at lower communication and energy cost. Charles Champagne Cossette et al propose a scheme to allow two devices to estimate their relative 3D position without an infrastructure [36]. This is different from the studies described previously which require anchors as references.…”

Agile Formation Control of Drone Flocking Enhanced with Active Vision-based Relative Localization

“…External UWB position system based on fixed anchors can estimate positions of agents in the common reference frame [18], but the requirement of the previous deployment of anchors means the framework can hardly be adopted in the unknown environment [8]. Efforts have been made to design an anchorfree UWB position scheme, such as UWB-IMU coupled approach [22], [23]. However, due to the single-dimensional distance measurement, the accuracy of relative localization relying barely on UWB is not satisfactory [22], [23].…”

“…Efforts have been made to design an anchorfree UWB position scheme, such as UWB-IMU coupled approach [22], [23]. However, due to the single-dimensional distance measurement, the accuracy of relative localization relying barely on UWB is not satisfactory [22], [23]. To achieve better estimation, UWB measurements are fused together with measurements from other sensors, such as VIO [18], wheel encoder [24], and optical flow [25], [26].…”

Visual Collaboration Leader-Follower UAV-Formation for Indoor Exploration

“…In GPS-denied indoor environments, other sensing modalities are deployed for estimating the drone position within a swarm. These include LiDaR sensing ( Kumar et al, 2017 ; Tsiourva and Papachristos, 2020 ; Yang et al, 2021 ), RSSI measurements ( Yokoyama et al, 2014 ; Xu et al, 2017 ; Shin et al, 2020 ), RF-based sensing ( Zhang et al, 2019 ; Shule et al, 2020 ; Cossette et al, 2021 ) and visual methods ( Lu et al, 2018 ).…”

“…The main concern with respect to LiDaR is the weight (close to 1Kg) and the high acquisition cost. RSSI and RF-based methods can be a viable alternative having a positioning noise deviation of 0.1m ( Cossette et al, 2021 ). The drawback stems with the requirement for precise placement of at least four external anchor nodes, with additional nodes required for multi-agent experimentation, and need of absence of metallic objects proximity to the antennas ( Suwatthikul et al, 2017 ).…”