ULOC: 超広帯域測距による大規模複雑環境での自己位置推定学習
ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges
アンカー位置が未知の大規模環境で、UWB測距と地図整合した自己位置推定ラベルを用いてMAMBAベースのネットワークを学習し、高精度な自己位置推定を実現した。
著者: Thien-Minh Nguyen, Yizhuo Yang, Tien-Dat Nguyen, Shenghai Yuan, Lihua Xie
分類: cs.RO, cs.LG
原文アブストラクト
While UWB-based methods can achieve high localization accuracy in small-scale areas, their accuracy and reliability are significantly challenged in large-scale environments. In this paper, we propose a learning-based framework named ULOC for Ultra-Wideband (UWB) based localization in such complex large-scale environments. First, anchors are deployed in the environment without knowledge of their actual position. Then, UWB observations are collected when the vehicle travels in the environment. At the same time, map-consistent pose estimates are developed from registering (onboard self-localization) data with the prior map to provide the training labels. We then propose a network based on MAMBA that learns the ranging patterns of UWBs over a complex large-scale environment. The experiment demonstrates that our solution can ensure high localization accuracy on a large scale compared to the state-of-the-art. We release our source code to benefit the community at https://github.com/brytsknguyen/uloc.
