AutoLoop: Fast Visual SLAM Fine-tuning through Agentic Curriculum Learning

TL;DR

AutoLoop introduces an automated curriculum learning approach using reinforcement learning to efficiently fine-tune visual SLAM systems, significantly reducing training time while maintaining or improving performance across multiple benchmarks.

Contribution

It presents a novel method combining automated curriculum learning with reinforcement learning to optimize loop closure weights in visual SLAM, eliminating manual hyperparameter tuning.

Findings

Reduces training time by an order of magnitude.

Achieves comparable or superior performance on multiple benchmarks.

Automates the weight tuning process for visual SLAM systems.

Abstract

Current visual SLAM systems face significant challenges in balancing computational efficiency with robust loop closure handling. Traditional approaches require careful manual tuning and incur substantial computational overhead, while learning-based methods either lack explicit loop closure capabilities or implement them through computationally expensive methods. We present AutoLoop, a novel approach that combines automated curriculum learning with efficient fine-tuning for visual SLAM systems. Our method employs a DDPG (Deep Deterministic Policy Gradient) agent to dynamically adjust loop closure weights during training, eliminating the need for manual hyperparameter search while significantly reducing the required training steps. The approach pre-computes potential loop closure pairs offline and leverages them through an agent-guided curriculum, allowing the model to adapt efficiently to new scenarios. Experiments conducted on TartanAir for training and validated across multiple benchmarks including KITTI, EuRoC, ICL-NUIM and TUM RGB-D demonstrate that AutoLoop achieves comparable or superior performance while reducing training time by an order of magnitude compared to traditional approaches. AutoLoop provides a practical solution for rapid adaptation of visual SLAM systems, automating the weight tuning process that traditionally requires multiple manual iterations. Our results show that this automated curriculum strategy not only accelerates training but also maintains or improves the model's performance across diverse environmental conditions.