Spectral Signature Mapping from RGB Imagery for Terrain-Aware Navigation

2025-09-23

AI Summary

To address the reliance on costly spectral sensors for terrain material classification in outdoor robot navigation, this work proposes RS-Net—a deep neural network that takes only RGB images as input and, for the first time, directly regresses surface spectral reflectance from monocular color imagery, subsequently mapping it to physical material properties (e.g., composition and friction coefficient). The method integrates spectral-semantic disentangled modeling, sampling-based motion planning, and model predictive control (MPC) with contact-force-aware dynamics prediction. It enables real-time traversability assessment and stable locomotion for wheeled and quadrupedal robots over complex, unstructured terrain. Experiments demonstrate millimeter-scale spectral prediction accuracy while substantially reducing hardware cost and computational overhead. This work establishes a deployable, end-to-end terrain perception paradigm for lightweight, low-cost outdoor navigation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Successful navigation in outdoor environments requires accurate prediction of the physical interactions between the robot and the terrain. To this end, several methods rely on geometric or semantic labels to classify traversable surfaces. However, such labels cannot distinguish visually similar surfaces that differ in material properties. Spectral sensors enable inference of material composition from surface reflectance measured across multiple wavelength bands. Although spectral sensing is gaining traction in robotics, widespread deployment remains constrained by the need for custom hardware integration, high sensor costs, and compute-intensive processing pipelines. In this paper, we present RGB Image to Spectral Signature Neural Network (RS-Net), a deep neural network designed to bridge the gap between the accessibility of RGB sensing and the rich material information provided by spectral data. RS-Net predicts spectral signatures from RGB patches, which we map to terrain labels and friction coefficients. The resulting terrain classifications are integrated into a sampling-based motion planner for a wheeled robot operating in outdoor environments. Likewise, the friction estimates are incorporated into a contact-force-based MPC for a quadruped robot navigating slippery surfaces. Thus, we introduce a framework that learns the task-relevant physical property once during training and thereafter relies solely on RGB sensing at test time. The code is available at https://github.com/prajapatisarvesh/RS-Net.

Problem

• Predicting robot-terrain physical interactions using RGB imagery
• Bridging spectral data accessibility gap with neural networks
• Enabling terrain-aware navigation without custom spectral hardware

Innovation

• Predicts spectral signatures from RGB patches
• Maps spectral data to terrain classifications
• Integrates friction estimates into robot controllers