移動ロボットの自己位置推定：モジュール式でオドメトリを改善するアプローチ
Mobile Robot Localization: a Modular, Odometry-Improving Approach
既存の自己位置推定アルゴリズムの出力とセンサ計測を融合するモジュール式アーキテクチャを提案し、絶対位置計測が失われても不確かさを推定してオドメトリを改善、実機実験で位置誤差を90%以上削減した。
著者: Luca Mozzarelli, Luca Cattaneo, Matteo Corno, Sergio Matteo Savaresi
分類: cs.RO
原文アブストラクト
Despite the number of works published in recent years, vehicle localization remains an open, challenging problem. While map-based localization and SLAM algorithms are getting better and better, they remain a single point of failure in typical localization pipelines. This paper proposes a modular localization architecture that fuses sensor measurements with the outputs of off-the-shelf localization algorithms. The fusion filter estimates model uncertainties to improve odometry in case absolute pose measurements are lost entirely. The architecture is validated experimentally on a real robot navigating autonomously proving a reduction of the position error of more than 90% with respect to the odometrical estimate without uncertainty estimation in a two-minute navigation period without position measurements.
