自律走行車の自己位置推定のためのエンドツーエンド学習に基づくマルチセンサフュージョン
An End-to-End Learning-Based Multi-Sensor Fusion for Autonomous Vehicle Localization
不確かさ推定を不要にし、高次ニューラルネット特徴でセンサ情報を符号化するエンドツーエンドのマルチセンサフュージョン手法を提案し、実環境で精度とロバスト性を向上させた。
著者: Changhong Lin, Jiarong Lin, Zhiqiang Sui, XiaoZhi Qu, Rui Wang, Kehua Sheng, Bo Zhang
分類: cs.RO
原文アブストラクト
Multi-sensor fusion is essential for autonomous vehicle localization, as it is capable of integrating data from various sources for enhanced accuracy and reliability. The accuracy of the integrated location and orientation depends on the precision of the uncertainty modeling. Traditional methods of uncertainty modeling typically assume a Gaussian distribution and involve manual heuristic parameter tuning. However, these methods struggle to scale effectively and address long-tail scenarios. To address these challenges, we propose a learning-based method that encodes sensor information using higher-order neural network features, thereby eliminating the need for uncertainty estimation. This method significantly eliminates the need for parameter fine-tuning by developing an end-to-end neural network that is specifically designed for multi-sensor fusion. In our experiments, we demonstrate the effectiveness of our approach in real-world autonomous driving scenarios. Results show that the proposed method outperforms existing multi-sensor fusion methods in terms of both accuracy and robustness. A video of the results can be viewed at https://youtu.be/q4iuobMbjME.
