Ultra-Wideband Aided UAV Positioning Using Incremental Smoothing with Ranges and Multilateration

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Barfoot

et al. 2022

We propose a fixed-lag smoother-based sensor fusion architecture to leverage the complementary benefits of rangebased sensors and visual-inertial odometry (VIO) for localization. We use two fixed-lag smoothers (FLS) to decouple accurate state estimation and high-rate pose generation for closed-loop control. The first FLS combines ultrawideband (UWB)-based range measurements and VIO to estimate the robot trajectory and any systematic biases that affect the range measurements in cluttered environments. The second FLS estimates smooth corrections to VIO to generate pose estimates at a high rate for online control. The proposed method is lightweight and can run on a computationally constrained micro-aerial vehicle (MAV). We validate our approach through closed-loop flight tests involving dynamic trajectories in multiple real-world cluttered indoor environments. Our method achieves decimeter-to-subdecimeter-level positioning accuracy using off-the-shelf sensors and decimeter-level tracking accuracy with minimally-tuned open-source controllers.

Section: Related Workmentioning

confidence: 78%

Gaussian Variational Inference with Covariance Constraints Applied to Range-only Localization

Goudar

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Barfoot

et al. 2022

“…Over the last decade, ultra-wideband (UWB) radio technology has been shown to provide high-accuracy time-of-arrival (TOA) measurements, making it a promising indoor localization solution. In autonomous indoor robotics [1], [2], the two main ranging schemes used for UWB localization are (i) twoway ranging (TWR) and (ii) time difference of arrival (TDOA). In TWR, the UWB module mounted on the robot (also called tag) communicates with a fixed UWB radio (also called anchor) and acquires range measurements through two-way communication.…”

Section: A Motivationmentioning

confidence: 99%

Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

IEEE Robot. Autom. Lett.

Goudar

Schoellig

2022

Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has recently emerged as a promising indoor positioning solution. However, in cluttered environments, both the UWB radio positions and the obstacle-induced nonline-of-sight (NLOS) measurement biases significantly impact the quality of the position estimate. Consequently, the placement of the UWB radios must be carefully designed to provide satisfactory localization accuracy for a region of interest. In this work, we propose a novel algorithm that optimizes the UWB radio positions for a pre-defined region of interest in the presence of obstacles. The mean-squared error (MSE) metric is used to formulate an optimization problem that balances the influence of the geometry of the radio positions and the NLOS effects. We further apply the proposed algorithm to compute a minimal number of UWB radios required for a desired localization accuracy and their corresponding positions. In a realworld cluttered environment, we show that the designed UWB radio placements provide 47% and 76% localization root-meansquared error (RMSE) reduction in 2D and 3D experiments, respectively, when compared against trivial placements.

“…In autonomous indoor robotics [4], [5], the two main ranging schemes used for UWB localization are (i) two-way ranging (TWR) and (ii) time difference of arrival (TDOA). The first is based on the time of flight (ToF) of a signal between an anchor (a fixed UWB radio, Figure 1) and a tag (a mobile robot).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots

Panerati

Schoellig

2021

Preprint

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices-making it especially suited for resource-constrained multi-robot applications. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. In this letter, we address these issues by proposing a robust UWB TDOA localization framework comprising of (i) learning-based bias correction and (ii) M-estimation-based robust filtering to handle outliers. The key properties of our approach are that (i) the learned biases generalize to different UWB anchor setups and (ii) the approach is computationally efficient enough to run on resource-constrained hardware. We demonstrate our approach on a Crazyflie nano-quadcopter. Experimental results show that the proposed localization framework, relying only on the onboard IMU and UWB, provides an average of 42.08% localization error reduction (in three different anchor setups) compared to the baseline approach without bias compensation. We also show autonomous trajectory tracking on a quadcopter using our UWB TDOA localization approach.