3D Robotics

Some academics at the University of Toronto have released a paper showing different techniques in correcting the position errors in the Crazyflie ultrawideband-based indoor localization tech.  None of them are perfect, but it's interesting to see what works best


Accurate indoor localization is a crucial enabling technology for many robotic applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) localization technology, in particular, has been shown to provide robust, high-resolution, and obstacle-penetrating ranging measurements. Nonetheless, UWB measurements are still corrupted by non-line-of-sight (NLOS) communication and spatially-varying biases due to doughnut-shaped antenna radiation pattern. In our recent work, we present a lightweight, two-step measurement correction method to improve the performance of both TWR and TDoA-based UWB localization.  We integrate our method into the Extended Kalman Filter (EKF) onboard a Crazyflie and demonstrate a closed-loop position estimation performance with ~20cm root-mean-square (RMS) error.

https://www.bitcraze.io/wp-content/uploads/2020/04/Selection_003-288x300.png 288w, https://www.bitcraze.io/wp-content/uploads/2020/04/Selection_003-768x801.png 768w, https://www.bitcraze.io/wp-content/uploads/2020/04/Selection_003-240x250.png 240w" sizes="(max-width: 459px) 100vw, 459px" /> A stylized depiction of our UWB indoor localization system and the schematics of the proposed estimation framework.


UWB measurement errors can be separated into two groups: (1) systematic bias caused by limitations in the UWB antenna pattern and (2) spurious measurements due to NLOS and multi-path propagation. We propose a two-step UWB bias correction approach exploiting machine learning (to address(1)) and statistical testing (to address (2)). The data-driven nature of our approach makes it agnostic to the origin of the measurement errors it corrects. "

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