Abstract
Accurate localization is a challenging task for autonomous vehicles, particularly in GPS-denied environments such as urban canyons and tunnels. In these scenarios, simultaneous localization and mapping (SLAM) offers a more robust alternative to GPS-based positioning, enabling vehicles to determine their position using onboard sensors and surrounding environment's landmarks. Among various vehicle SLAM approaches, Rao-Blackwellized particle filter (RBPF) stands out as one of the most widely adopted methods due to its efficient solution with logarithmic complexity relative to the map size. RBPF approximates the posterior distribution of the vehicle pose using a set of Monte Carlo particles through two main steps: sampling and importance weighting. The key to effective sampling lies in solving a distribution that closely approximates the posterior, known as the sampling distribution, to accelerate convergence. Existing methods typically derive this distribution via linearization, which introduces significant approximation errors due to the inherent nonlinearity of the system. To address this limitation, we propose a novel vehicle SLAM method called \textit{N}atural Gr\textit{a}dient Gaussia\textit{n} Appr\textit{o}ximation (NANO)-SLAM, which avoids linearization errors by modeling the sampling distribution as the solution to an optimization problem over Gaussian parameters and solving it using natural gradient descent. This approach improves the accuracy of the sampling distribution and consequently enhances localization performance. Experimental results on the long-distance Sydney Victoria Park vehicle SLAM dataset show that NANO-SLAM achieves over 50\% improvement in localization accuracy compared to the most widely used vehicle SLAM algorithms, with minimal additional computational cost.
Abstract (translated)
准确的定位是自主车辆面临的挑战，特别是在没有GPS信号的环境中，如城市峡谷和隧道。在这种情况下，同时定位与地图构建（SLAM）提供了一个比基于GPS的位置服务更为稳健的选择，使车辆能够利用车载传感器和周围环境中的地标来确定自己的位置。在众多车辆SLAM方法中，Rao-Blackwellized粒子滤波器（RBPF）因其对地图大小具有对数复杂度的高效解决方案而被广泛采用。RBPF通过两个主要步骤——采样和重要性加权——利用一组蒙特卡洛粒子来近似车辆姿态的后验分布。有效采样的关键在于求解一个能够紧密逼近后验分布的分布，即采样分布，从而加速收敛过程。现有方法通常通过线性化导出该分布，但由于系统固有的非线性特性，这会导致显著的近似误差。 为了解决这一局限性，我们提出了一种新的车辆SLAM方法——自然梯度高斯逼近（NANO）-SLAM，这种方法通过将采样分布建模为一个关于高斯参数优化问题的解，并利用自然梯度下降法进行求解来避免线性化误差。这种做法提高了采样分布的准确性，从而提升了定位性能。 在长距离悉尼维多利亚公园车辆SLAM数据集上的实验结果显示，NANO-SLAM相较于最广泛使用的车辆SLAM算法，在定位精度上实现了超过50%的改进，并且几乎不需要增加额外的计算成本。
URL
https://arxiv.org/abs/2504.19195
