Visual-inertial SLAM aided estimation of anchor poses and sensor error model parameters of UWB radio modules

Abstract

Local positioning technologies based on ultrawideband (UWB) ranging have become broadly available and accurate enough for various robotic applications. In an infrastructure setup with static anchor radio modules one common problem is to determine their global positions within the world coordinate frame. Furthermore, issues like the complex radiofrequency wave propagation properties make it difficult to design a consistent sensor error model which generalizes well across different anchor setups and environments. Combining radio based local positioning systems with a visual-inertial navigation system (VINS) can provide very accurate pose estimates for calibration of the radio based localization modules and at the same time alleviate the inherent drift in visualinertial navigation. We propose an approach to utilize a visualinertial SLAM system using fish-eye stereo cameras and an IMU to estimate the anchor 6D poses as well as the parameters of an UWB module sensor error model on a micro-aerial-vehicle (MAV). Fiducial markers on all anchor radio modules are used as artificial landmarks within the SLAM system to get accurate anchor module pose estimates.

Sensor Information Sharing Using a Producer-Consumer Algorithm on Small Vehicles

“…The most recent research efforts present various technologies to achieve a good precision in detecting the global and relative position and thus perform a satisfactory SLAM. On the other hand, UWB and vision systems have been combined to improve the estimation of the position in the workspace [ 23 ]. Other strategies bet on the fusion of UWB data with those coming from INS [ 24 ], and Optitrack [ 25 ].…”

A Novel Ultra-Wideband Double Difference Indoor Positioning Method with Additional Baseline Constraint

“…The electrical delay is time-independent and only related to electronic devices and antenna types. It is also defined as the antenna delay in some articles [23]. The base station coordinate error is small, since the base station coordinate is generally obtained by other high-precision measurement technologies.…”

“…Wang et al [21] proposed a zero-velocity update (ZUPT)/UWB fusion algorithm based on graph optimization, with an accuracy of 0.4 m. The same idea was also presented by Zhang et al [22]. Lutz et al [23] proposed to combine UWB and a visual-inertial navigation system (VINS) to provide accurate pose estimations. Additionally, the drift in VINS could be alleviated.…”