同時モデル選択と状態変数決定によるセンサモデル同定
Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
未知の計測データ時系列に対し、事前定義されたセンサモデル群から最も確からしいモデルを自動選択し、キャリブレーション状態や参照フレームの必要性も同時に推定する手法を提案。
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著者: Christian Brommer, Alessandro Fornasier, Jan Steinbrener, Stephan Weiss
分類: cs.RO, cs.IT, cs.NA, cs.SY, eess.SY, math.IT, math.NA
原文アブストラクト
We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the most likely sensor model for a time series of unknown measurement data, given an extendable catalog of predefined sensor models. Sensor model definitions may require states for rigid-body calibrations and dedicated reference frames to replicate a measurement based on the robot's localization state. A health metric is introduced, which verifies the outcome of the selection process in order to detect false positives and facilitate reliable decision-making. In a second stage, an initial guess for identified calibration states is generated, and the necessity of sensor world reference frames is evaluated. The identified sensor model with its parameter information is then used to parameterize and initialize a state estimation application, thus ensuring a more accurate and robust integration of new sensor elements. This method is helpful for inexperienced users who want to identify the source and type of a measurement, sensor calibrations, or sensor reference frames. It will also be important in the field of modular multi-agent scenarios and modularized robotic platforms that are augmented by sensor modalities during runtime. Overall, this work aims to provide a simplified integration of sensor modalities to downstream applications and circumvent common pitfalls in the usage and development of localization approaches.
