GHNet: 速度計測からGNSS方位角を学習
GHNet:Learning GNSS Heading from Velocity Measurements
低速走行時でもGNSSの速度計測から深層学習で高精度に方位角を推定するフレームワークGHNetを提案し、従来のモデルベース手法を上回る性能を示した。
著者: Nitzan Dahan, Itzik Klein
分類: cs.RO
原文アブストラクト
By utilizing global navigation satellite system (GNSS) position and velocity measurements, the fusion between the GNSS and the inertial navigation system provides accurate and robust navigation information. When considering land vehicles,like autonomous ground vehicles,off-road vehicles or mobile robots,a GNSS-based heading angle measurement can be obtained and used in parallel to the position measurement to bound the heading angle drift. Yet, at low vehicle speeds (less than 2m/s) such a model-based heading measurement fails to provide satisfactory performance. This paper proposes GHNet, a deep-learning framework capable of accurately regressing the heading angle for vehicles operating at low speeds. We demonstrate that GHNet outperforms the current model-based approach for simulation and experimental datasets.
