Most autonomous vehicles rely on accurate and efficient localization, which is achieved by comparing live sensor data to a preexisting map, to navigate their environment. Balancing the accuracy of localization with computational efficiency remains a significant challenge, as high-accuracy methods often come with higher computational costs. In this paper, we present two ways of improving lidar localization efficiency and study their impact on performance. First, we integrate two lightweight odometry estimators, a correspondence-free Doppler-inertial estimator and a low-cost wheel odometer-gyroscope (OG) method, into a topometric localization pipeline and compare them against a state-of-the-art (SOTA) iterative closest point (ICP) baseline. We highlight the trade-offs between these approaches: the Doppler and OG estimators offer faster, lightweight updates, while ICP provides higher accuracy at the cost of increased computational load. Second, by controlling the frequency of localization updates and leveraging odometry estimates between them, we demonstrate that accurate localization can be maintained while optimizing for computational efficiency using any of the presented methods. We evaluate these approaches using over 100 km of unique real-world driving data in different on-road environments. By varying the localization interval, we demonstrate that computational effort can be reduced by 27%, 80%, and 91% for the ICP, Doppler, and OG estimators, respectively, while maintaining SOTA accuracy.

翻译：大多数自动驾驶车辆依赖精确且高效的定位来导航其环境，这通过将实时传感器数据与预先构建的地图进行比对来实现。在定位精度与计算效率之间寻求平衡仍是一项重大挑战，因为高精度方法往往伴随着更高的计算成本。本文提出了两种提升激光雷达定位效率的方法，并研究了它们对性能的影响。首先，我们将两种轻量级里程计估计器——一种免对应点匹配的多普勒惯性估计器和一种低成本轮式里程计-陀螺仪（OG）方法——集成到拓扑度量定位流水线中，并将其与现有最优（SOTA）迭代最近点（ICP）基线方法进行对比。我们着重分析了这些方法之间的权衡：多普勒和OG估计器能够提供更快速、轻量化的更新，而ICP虽以更高计算负载为代价，却可提供更高精度。其次，通过控制定位更新的频率并利用两次更新之间的里程计估计值，我们证明使用所提出的任何方法均可维持高精度定位，同时优化计算效率。我们利用超过100公里的真实道路驾驶数据（涵盖不同道路环境）对这些方法进行了评估。通过改变定位间隔，我们证明在保持SOTA精度的前提下，ICP、多普勒和OG估计器的计算量可分别减少27%、80%和91%。