用障碍法让因子图搞定不等式约束，提升自动驾驶控制效率
Barrier Method for Inequality Constrained Factor Graph Optimization with Application to Model Predictive Control
- 将障碍内点法融入因子图，用对数障碍函数处理不等式约束
- 在自适应巡航控制中实现更快收敛，计算效率优于现有方法
- 首个支持等式与不等式约束统一求解的g2o扩展，开源可用
因子图在机器人感知任务中表现出色，尤其在定位与建图方面。然而，在最优控制问题（尤其是模型预测控制，MPC）中的应用受限于约束处理的根本挑战。本文提出将障碍内点法（BIPM）与因子图结合，作为g2o框架的开源扩展。通过引入专门设计的不等式因子节点，编码对数障碍函数，突破了传统因子图仅能处理二次型约束的局限。据我们所知，这是首个基于g2o、能在统一优化后端中高效处理等式与不等式约束的方法。我们在自动驾驶车辆的多目标自适应巡航控制任务中验证了该方法，与先进约束处理技术的基准对比显示，收敛速度更快，计算效率更高。（代码仓库：https://github.com/snt-arg/bipm_g2o）
原文摘要 · Abstract (English)
Factor graphs have demonstrated remarkable efficiency for robotic perception tasks, particularly in localization and mapping applications. However, their application to optimal control problems -- especially Model Predictive Control (MPC) -- has remained limited due to fundamental challenges in constraint handling. This paper presents a novel integration of the Barrier Interior Point Method (BIPM) with factor graphs, implemented as an open-source extension to the widely adopted g2o framework. Our approach introduces specialized inequality factor nodes that encode logarithmic barrier functions, thereby overcoming the quadratic-form limitations of conventional factor graph formulations. To the best of our knowledge, this is the first g2o-based implementation capable of efficiently handling both equality and inequality constraints within a unified optimization backend. We validate the method through a multi-objective adaptive cruise control application for autonomous vehicles. Benchmark comparisons with state-of-the-art constraint-handling techniques demonstrate faster convergence and improved computational efficiency. (Code repository: https://github.com/snt-arg/bipm_g2o)
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