用可微分方法训练粒子平滑器，提升复杂场景下的定位精度。
Learning to be Smooth: An End-to-End Differentiable Particle Smoother
- 设计双向粒子流融合的可微分平滑框架，支持端到端训练。
- 在真实城市视频与地图上实现比现有方法更精确的车辆全局定位。
- 适合需要高精度时空推理的视觉与机器人任务。
在视觉与机器人等挑战性状态估计问题中，基于粒子的表示能有效进行多后验模式的时序推理。粒子平滑器通过前后向信息传播，有望实现更精准的离线数据分析，但传统方法依赖人工设计的动力学与观测模型。本文基于判别式训练的粒子滤波最新进展，提出一种在长序列中低方差传播梯度的框架。所提出的“双滤波”平滑器整合前后向传播的粒子流，并在重采样中引入分层与重要性权重，以获得神经网络动力学与观测模型的低方差梯度估计。结果表明，该混合密度粒子平滑器在真实世界视频与地图上的城市级全局车辆定位任务中，显著优于当前最优的粒子滤波器及搜索类基线方法。
原文摘要 · Abstract (English)
For challenging state estimation problems arising in domains like vision and robotics, particle-based representations attractively enable temporal reasoning about multiple posterior modes. Particle smoothers offer the potential for more accurate offline data analysis by propagating information both forward and backward in time, but have classically required human-engineered dynamics and observation models. Extending recent advances in discriminative training of particle filters, we develop a framework for low-variance propagation of gradients across long time sequences when training particle smoothers. Our "two-filter'' smoother integrates particle streams that are propagated forward and backward in time, while incorporating stratification and importance weights in the resampling step to provide low-variance gradient estimates for neural network dynamics and observation models. The resulting mixture density particle smoother is substantially more accurate than state-of-the-art particle filters, as well as search-based baselines, for city-scale global vehicle localization from real-world videos and maps.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。