Efficient Active Training for Deep LiDAR Odometry

2025-09-03

AI Summary

Deep LiDAR odometry suffers from high data requirements and poor generalization across diverse environments. To address this, we propose a two-stage active learning framework for data selection. First, we identify high-information initial training sequences by jointly analyzing trajectory consistency and scene reconstruction inconsistency. Second, we model motion sequences as node-edge graphs and iteratively select optimal samples by combining predictive uncertainty estimation with incremental learning. Our method significantly reduces data dependency—achieving full-dataset accuracy using only 52% of the original training data—while demonstrating superior robustness and training efficiency across multi-weather conditions. This work establishes a new paradigm for lightweight, adaptive LiDAR odometry training.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a rich initial training dataset for training the base model. For complex sequences that are difficult to analyze, especially under challenging snowy weather conditions, AIS uses scene reconstruction and prediction inconsistency to iteratively select training samples, refining the model to handle a wide range of real-world scenarios. Experiments across datasets and weather conditions validate our approach's effectiveness. Notably, our method matches the performance of full-dataset training with just 52% of the sequence volume, demonstrating the training efficiency and robustness of our active training paradigm. By optimizing the training process, our approach sets the stage for more agile and reliable LiDAR odometry systems, capable of navigating diverse environmental conditions with greater precision.

Problem

• Reducing training data needs for LiDAR odometry models
• Enhancing model generalization across diverse environments
• Improving efficiency in challenging weather conditions

Innovation

• Active training framework for selective data extraction
• ITSS and AIS strategies for diverse sequence analysis
• Scene reconstruction and inconsistency for sample selection