Balancing Act: Trading Off Doppler Odometry and Map Registration for Efficient Lidar Localization

2025-03-03

AI Summary

Achieving both high-accuracy localization and real-time performance with LiDAR in autonomous driving remains challenging. Method: This paper proposes a synergistic optimization framework integrating Doppler LiDAR odometry (DLO) with map matching. We first systematically quantify the accuracy–efficiency trade-off between DLO and iterative closest point (ICP) registration. Then, we design a localization sparsification strategy based on dynamically adjusted update frequency, incorporating a lightweight motion prior within a topology–metric hybrid framework to enable controllable accuracy–efficiency balancing. Results: Evaluated on over 100 km of real-world road data, the method achieves translational error < 0.05 m and rotational error < 0.1°, while reducing ICP computational load by over 30%. The optimal update interval is determined as one localization update per 10 LiDAR frames, significantly improving computational efficiency without compromising sub-decimeter accuracy.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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 a lightweight Doppler-based odometry method into a topometric localization pipeline and compare its performance against an iterative closest point (ICP)-based method. We highlight the trade-offs between these approaches: the Doppler estimator offers 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 either odometry method. Our experimental results show that localizing every 10 lidar frames strikes a favourable balance, achieving a localization accuracy below 0.05 meters in translation and below 0.1 degrees in orientation while reducing computational effort by over 30% in an ICP-based pipeline. We quantify the trade-off of accuracy to computational effort using over 100 kilometers of real-world driving data in different on-road environments.

Problem

• Balancing localization accuracy and computational efficiency.
• Integrating Doppler odometry for lightweight localization updates.
• Optimizing update frequency to reduce computational load.

Innovation

• Integrates Doppler odometry for lightweight updates
• Controls update frequency to optimize efficiency
• Balances accuracy and computational load effectively