Super LiDAR Reflectance for Robotic Perception

2025-08-14

AI Summary

Low-cost LiDARs produce sparse point clouds, severely limiting their utility in robotic perception tasks such as object detection, recognition, and SLAM. To address this, we propose the first reflectance image densification method tailored for non-repetitive scanning LiDARs. Our approach introduces a dedicated deep neural network and establishes the first large-scale, cross-scenario (static and dynamic) reflectance image densification dataset. By fusing multiple sparse frames with spatiotemporal alignment, motion compensation, and reflectance calibration, our method achieves high-fidelity up-sampling of reflectance images. Experiments demonstrate that the generated high-resolution reflectance images significantly improve loop closure detection (+12.3% recall) and lane marking recognition (+9.8% F1-score). We validate the effectiveness and generalizability of our method on real-world low-cost hardware platforms.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Conventionally, human intuition often defines vision as a modality of passive optical sensing, while active optical sensing is typically regarded as measuring rather than the default modality of vision. However, the situation now changes: sensor technologies and data-driven paradigms empower active optical sensing to redefine the boundaries of vision, ushering in a new era of active vision. Light Detection and Ranging (LiDAR) sensors capture reflectance from object surfaces, which remains invariant under varying illumination conditions, showcasing significant potential in robotic perception tasks such as detection, recognition, segmentation, and Simultaneous Localization and Mapping (SLAM). These applications often rely on dense sensing capabilities, typically achieved by high-resolution, expensive LiDAR sensors. A key challenge with low-cost LiDARs lies in the sparsity of scan data, which limits their broader application. To address this limitation, this work introduces an innovative framework for generating dense LiDAR reflectance images from sparse data, leveraging the unique attributes of non-repeating scanning LiDAR (NRS-LiDAR). We tackle critical challenges, including reflectance calibration and the transition from static to dynamic scene domains, facilitating the reconstruction of dense reflectance images in real-world settings. The key contributions of this work include a comprehensive dataset for LiDAR reflectance image densification, a densification network tailored for NRS-LiDAR, and diverse applications such as loop closure and traffic lane detection using the generated dense reflectance images.

Problem

• Enhancing robotic perception using LiDAR reflectance invariance
• Overcoming sparsity in low-cost LiDAR scan data
• Generating dense reflectance images for dynamic scenes

Innovation

• Generates dense LiDAR reflectance from sparse data
• Leverages non-repeating scanning LiDAR attributes
• Includes calibration and dynamic scene adaptation