Abstract In the context of autonomous driving in urban environments accurate and reliable information about the vehicle motion is crucial. This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Moreover, in order to enhance the estimation accuracy, the scheme simultaneously estimates the gyroscope and accelerometer biases. The approach relies on a state-affine representation of a kinematic model with an additional measurement equation based on a single-track model. The sensor fusion scheme is built upon a recently proposed adaptive estimator, which allows a direct consideration of model uncertainties and sensor noise. In order to provide accurate estimates during collision avoidance manoeuvres, a measurement covariance adaptation is introduced, which reduces the influence of the single-track model when its information is superfluous. A validation using experimental data demonstrates the effectiveness of the method during both regular urban drives and collision avoidance manoeuvres.

中文翻译：

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用于高精度车辆运动状态估计的多模态传感器融合

摘要 在城市环境中自动驾驶的背景下，关于车辆运动的准确可靠的信息至关重要。本文提出了一种多模态传感器融合方案，该方案基于标准量产汽车传感器和惯性测量单元，估计三维车辆速度和姿态角（俯仰和侧倾）。此外，为了提高估计精度，该方案同时估计陀螺仪和加速度计偏差。该方法依赖于运动学模型的状态仿射表示，以及基于单轨模型的附加测量方程。传感器融合方案建立在最近提出的自适应估计器之上，它允许直接考虑模型不确定性和传感器噪声。为了在防撞机动过程中提供准确的估计，引入了测量协方差自适应，这减少了信息多余时单轨模型的影响。使用实验数据进行的验证证明了该方法在常规城市驾驶和防撞机动中的有效性。