PIDLoc: Cross-View Pose Optimization Network Inspired by PID Controllers

2025-03-04

AI Summary

To address insufficient accuracy and robustness of cross-view localization in GNSS-denied environments (e.g., urban canyons), this paper proposes an end-to-end cross-view pose optimization network inspired by PID control principles. Methodologically, it pioneers the integration of proportional (P), integral (I), and derivative (D) mechanisms into pose regression: the P branch models local feature discrepancies; the I branch accumulates historical global pose errors to enhance stability; and the D branch enables fine-grained correction via gradient-based refinement. A spatially aware pose estimator (SPE) is further introduced to improve geometric consistency. The network fuses multi-scale features from RGB images and LiDAR point clouds, incorporating spatial attention and a differentiable pose regression head. Evaluated on the KITTI dataset, it achieves state-of-the-art performance—reducing positional error by 37.8% over prior best methods—and demonstrates significantly improved robustness under large initial pose errors.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Browser and end-point security

Abstract

Accurate localization is essential for autonomous driving, but GNSS-based methods struggle in challenging environments such as urban canyons. Cross-view pose optimization offers an effective solution by directly estimating vehicle pose using satellite-view images. However, existing methods primarily rely on cross-view features at a given pose, neglecting fine-grained contexts for precision and global contexts for robustness against large initial pose errors. To overcome these limitations, we propose PIDLoc, a novel cross-view pose optimization approach inspired by the proportional-integral-derivative (PID) controller. Using RGB images and LiDAR, the PIDLoc comprises the PID branches to model cross-view feature relationships and the spatially aware pose estimator (SPE) to estimate the pose from these relationships. The PID branches leverage feature differences for local context (P), aggregated feature differences for global context (I), and gradients of feature differences for precise pose adjustment (D) to enhance localization accuracy under large initial pose errors. Integrated with the PID branches, the SPE captures spatial relationships within the PID-branch features for consistent localization. Experimental results demonstrate that the PIDLoc achieves state-of-the-art performance in cross-view pose estimation for the KITTI dataset, reducing position error by $37.8%$ compared with the previous state-of-the-art.

Problem

• Improves vehicle localization accuracy in challenging environments
• Addresses limitations in cross-view pose optimization methods
• Enhances robustness against large initial pose errors

Innovation

• PIDLoc uses PID controller-inspired cross-view optimization.
• Integrates RGB images and LiDAR for pose estimation.
• SPE captures spatial relationships for consistent localization.