Abstract
Particle filter-based 2D-SLAM is widely used in indoor localization tasks due to its efficiency. However, indoor environments such as long straight corridors can cause severe degeneracy problems in SLAM. In this paper, we use Proximal Policy Optimization (PPO) to train an adaptive degeneracy optimization agent (DOA) to address degeneracy problem. We propose a systematic methodology to address three critical challenges in traditional supervised learning frameworks: (1) data acquisition bottlenecks in degenerate dataset, (2) inherent quality deterioration of training samples, and (3) ambiguity in annotation protocol design. We design a specialized reward function to guide the agent in developing perception capabilities for degenerate environments. Using the output degeneracy factor as a reference weight, the agent can dynamically adjust the contribution of different sensors to pose optimization. Specifically, the observation distribution is shifted towards the motion model distribution, with the step size determined by a linear interpolation formula related to the degeneracy factor. In addition, we employ a transfer learning module to endow the agent with generalization capabilities across different environments and address the inefficiency of training in degenerate environments. Finally, we conduct ablation studies to demonstrate the rationality of our model design and the role of transfer learning. We also compare the proposed DOA with SOTA methods to prove its superior degeneracy detection and optimization capabilities across various environments.
Abstract (translated)
基于粒子滤波的2D-SLAM（同时定位与地图构建）方法由于其效率，在室内定位任务中被广泛应用。然而，如长直走廊等室内环境可能会导致SLAM产生严重的退化问题。本文中，我们采用近端策略优化（PPO）训练一个自适应退化解算器代理（DOA），以解决这种退化问题。文中提出了一种系统方法来应对传统监督学习框架中的三个关键挑战：1）退化数据集的数据获取瓶颈；2）训练样本固有的质量下降问题；3）注释协议设计的模糊性。 我们为该代理设计了一个专门化的奖励函数，以引导其在退化环境中发展感知能力。通过使用输出退化因子作为参考权重，代理能够动态调整不同传感器对姿态优化贡献的比例。具体而言，观察分布会向运动模型分布进行偏移，并且步长由与退化因子相关的线性插值公式确定。 此外，我们还采用了一个迁移学习模块，以赋予该代理跨不同环境的泛化能力，从而解决在退化环境中训练效率低下的问题。最后，通过消融研究证明了我们的模型设计的合理性以及迁移学习的作用，并且与最先进的方法进行了比较，验证了DOA在各种环境中的优越退化解算和优化能力。
URL
https://arxiv.org/abs/2507.19742
