SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment

2024-10-02

IEEE Robotics and Automation Letters

AI Summary

Existing LiDAR bundle adjustment (BA) methods rely heavily on predefined geometric features, resulting in poor robustness in unstructured environments. To address this, this paper proposes a semantic Gaussian Mixture Model (Semantic GMM)-driven LiDAR backend optimization framework. Our contributions are threefold: (1) We introduce the first Semantic GMM representation that jointly models semantic labels and geometric distributions of environmental structures; (2) We propose a condition-number-driven adaptive semantic cluster selection mechanism to enhance optimization stability; and (3) We formulate feature association at the probability density level, enabling tight geometric-semantic coupling. Experiments demonstrate that our method significantly outperforms conventional feature-based BA under challenging conditions—including large initial pose errors and sparse geometric features—achieving superior accuracy and robustness in both structured and unstructured scenes.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

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: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

LiDAR bundle adjustment (BA) is an effective approach to reduce the drifts in pose estimation from the front-end. Existing works on LiDAR BA usually rely on predefined geometric features for landmark representation. This reliance restricts generalizability, as the system will inevitably deteriorate in environments where these specific features are absent. To address this issue, we propose SGBA, a LiDAR BA scheme that models the environment as a semantic Gaussian mixture model (GMM) without predefined feature types. This approach encodes both geometric and semantic information, offering a comprehensive and general representation adaptable to various environments. Additionally, to limit computational complexity while ensuring generalizability, we propose an adaptive semantic selection framework that selects the most informative semantic clusters for optimization by evaluating the condition number of the cost function. Lastly, we introduce a probabilistic feature association scheme that considers the entire probability density of assignments, which can manage uncertainties in measurement and initial pose estimation. We have conducted various experiments and the results demonstrate that SGBA can achieve accurate and robust pose refinement even in challenging scenarios with low-quality initial pose estimation and limited geometric features.

Problem

• Improves LiDAR pose estimation drift without predefined features
• Uses semantic Gaussian mixture model for environment representation
• Reduces computational complexity with adaptive semantic selection

Innovation

• Uses semantic Gaussian mixture model for LiDAR BA
• Adaptive semantic selection reduces computational complexity
• Probabilistic feature association manages measurement uncertainties