Uncertainty-aware Accurate Elevation Modeling for Off-road Navigation via Neural Processes

2025-08-05

AI Summary

To address the challenges of real-time, high-precision terrain elevation modeling and uncertainty quantification in off-road navigation, this paper proposes a neural process-based multimodal elevation modeling framework. The method fuses LiDAR and camera semantic features and introduces a local spherical query attention mechanism, preserving spatial geometric structure while reducing computational complexity by 17%. Leveraging neural processes, it enables robust interpolation and extrapolation over unstructured terrains—including trails, deserts, and hills—and simultaneously produces well-calibrated uncertainty estimates. Evaluated on a real-world unpaved terrain dataset, our approach significantly outperforms Gaussian processes and state-of-the-art neural network baselines, achieving concurrent improvements in prediction accuracy, uncertainty calibration, and inference efficiency. This advances safe, autonomous off-road mobility by delivering reliable, uncertainty-aware terrain perception.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision

Application Category

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

Abstract

Terrain elevation modeling for off-road navigation aims to accurately estimate changes in terrain geometry in real-time and quantify the corresponding uncertainties. Having precise estimations and uncertainties plays a crucial role in planning and control algorithms to explore safe and reliable maneuver strategies. However, existing approaches, such as Gaussian Processes (GPs) and neural network-based methods, often fail to meet these needs. They are either unable to perform in real-time due to high computational demands, underestimating sharp geometry changes, or harming elevation accuracy when learned with uncertainties. Recently, Neural Processes (NPs) have emerged as a promising approach that integrates the Bayesian uncertainty estimation of GPs with the efficiency and flexibility of neural networks. Inspired by NPs, we propose an effective NP-based method that precisely estimates sharp elevation changes and quantifies the corresponding predictive uncertainty without losing elevation accuracy. Our method leverages semantic features from LiDAR and camera sensors to improve interpolation and extrapolation accuracy in unobserved regions. Also, we introduce a local ball-query attention mechanism to effectively reduce the computational complexity of global attention by 17% while preserving crucial local and spatial information. We evaluate our method on off-road datasets having interesting geometric features, collected from trails, deserts, and hills. Our results demonstrate superior performance over baselines and showcase the potential of neural processes for effective and expressive terrain modeling in complex off-road environments.

Problem

• Real-time terrain elevation modeling with uncertainty quantification
• Overcoming computational inefficiency in existing elevation estimation methods
• Improving accuracy in sharp elevation changes and unobserved regions

Innovation

• Neural Processes integrate Bayesian uncertainty with neural networks
• Leverages LiDAR and camera semantic features for accuracy
• Local ball-query attention reduces computational complexity