Enhancing Lidar Point Cloud Sampling via Colorization and Super-Resolution of Lidar Imagery

2025-05-04

AI Summary

To address registration mismatches and odometry drift caused by LiDAR point cloud sparsity and geometric degeneracy (e.g., long corridors), this paper proposes a novel framework leveraging LiDAR-image coloring and super-resolution enhancement. Specifically, we introduce image-level colorization and single-image super-resolution (SISR) techniques to LiDAR-encoded depth, reflectance, and near-infrared (NIR) images—without requiring camera calibration—to jointly embed semantic and geometric priors. This enhances keypoint detection robustness and guides high-fidelity point cloud downsampling and registration. Evaluated on public benchmarks, our method significantly reduces both translational and rotational registration errors. Under stringent point-count constraints, it achieves superior registration accuracy and odometry stability compared to state-of-the-art pure-LiDAR approaches. The framework effectively mitigates challenges arising from insufficient geometric structure and misleading outliers.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• 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

Abstract

Recent advancements in lidar technology have led to improved point cloud resolution as well as the generation of 360 degrees, low-resolution images by encoding depth, reflectivity, or near-infrared light within each pixel. These images enable the application of deep learning (DL) approaches, originally developed for RGB images from cameras to lidar-only systems, eliminating other efforts, such as lidar-camera calibration. Compared with conventional RGB images, lidar imagery demonstrates greater robustness in adverse environmental conditions, such as low light and foggy weather. Moreover, the imaging capability addresses the challenges in environments where the geometric information in point clouds may be degraded, such as long corridors, and dense point clouds may be misleading, potentially leading to drift errors. Therefore, this paper proposes a novel framework that leverages DL-based colorization and super-resolution techniques on lidar imagery to extract reliable samples from lidar point clouds for odometry estimation. The enhanced lidar images, enriched with additional information, facilitate improved keypoint detection, which is subsequently employed for more effective point cloud downsampling. The proposed method enhances point cloud registration accuracy and mitigates mismatches arising from insufficient geometric information or misleading extra points. Experimental results indicate that our approach surpasses previous methods, achieving lower translation and rotation errors while using fewer points.

Problem

• Improves Lidar point cloud sampling via colorization and super-resolution
• Enhances keypoint detection for better odometry estimation accuracy
• Reduces errors in point cloud registration with fewer points

Innovation

• DL-based colorization enhances lidar imagery
• Super-resolution improves lidar image quality
• Enhanced images aid keypoint detection accuracy