用神经网络预测激光雷达定位误差，提升自动驾驶定位精度。
Neural Error Covariance Estimation for Precise LiDAR Localization
- 用神经网络学习激光雷达地图匹配的误差协方差。
- 在卡尔曼滤波中使用该估计，定位精度提升2厘米。
- 专为误差协方差设计新数据生成方法，适合高精定位研究者。
自动驾驶因技术进步和交通变革潜力受到广泛关注。该领域关键挑战是精确定位，尤其是基于激光雷达的地图匹配，易受数据退化影响而产生误差。多数传感器融合方法（如卡尔曼滤波）依赖各传感器的准确误差协方差以提升定位精度，但地图匹配的可靠协方差估计仍具挑战性。为此，我们提出一种基于神经网络的框架，用于预测激光雷达地图匹配中的定位误差协方差。为实现此目标，我们设计了一种专用于误差协方差估计的新数据生成方法。在使用卡尔曼滤波的评估中，定位精度提升了2厘米，显著改善了该领域性能。
原文摘要 · Abstract (English)
Autonomous vehicles have gained significant attention due to technological advancements and their potential to transform transportation. A critical challenge in this domain is precise localization, particularly in LiDAR-based map matching, which is prone to errors due to degeneracy in the data. Most sensor fusion techniques, such as the Kalman filter, rely on accurate error covariance estimates for each sensor to improve localization accuracy. However, obtaining reliable covariance values for map matching remains a complex task. To address this challenge, we propose a neural network-based framework for predicting localization error covariance in LiDAR map matching. To achieve this, we introduce a novel dataset generation method specifically designed for error covariance estimation. In our evaluation using a Kalman filter, we achieved a 2 cm improvement in localization accuracy, a significant enhancement in this domain.
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