End-to-End LiDAR optimization for 3D point cloud registration

2026-02-11

AI Summary

This work proposes the first adaptive LiDAR sensing framework that bridges the gap between data acquisition and downstream point cloud registration tasks. Unlike conventional LiDAR systems that employ fixed sensing parameters—often leading to redundant or insufficient data and high computational costs—our approach integrates registration performance feedback directly into the sensing process. By jointly optimizing acquisition parameters and registration hyperparameters in an end-to-end manner, the framework dynamically balances point cloud density, noise, and sparsity. Evaluated on the CARLA simulation platform, the method significantly outperforms fixed-parameter baselines, achieving higher registration accuracy and efficiency while maintaining strong generalization capabilities, thereby transcending the limitations of traditional static perception paradigms.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

LiDAR sensors are a key modality for 3D perception, yet they are typically designed independently of downstream tasks such as point cloud registration. Conventional registration operates on pre-acquired datasets with fixed LiDAR configurations, leading to suboptimal data collection and significant computational overhead for sampling, noise filtering, and parameter tuning. In this work, we propose an adaptive LiDAR sensing framework that dynamically adjusts sensor parameters, jointly optimizing LiDAR acquisition and registration hyperparameters. By integrating registration feedback into the sensing loop, our approach optimally balances point density, noise, and sparsity, improving registration accuracy and efficiency. Evaluations in the CARLA simulation demonstrate that our method outperforms fixed-parameter baselines while retaining generalization abilities, highlighting the potential of adaptive LiDAR for autonomous perception and robotic applications.

Problem

• LiDAR optimization
• 3D point cloud registration
• adaptive sensing
• sensor configuration
• end-to-end optimization

Innovation

• adaptive LiDAR
• point cloud registration
• end-to-end optimization
• sensor-task co-design
• 3D perception