[论文解读] SLAM-Safe Planner: Preventing Monocular SLAM Failure using Reinforcement Learning.

Vignesh Prasad, Saurabh Singh+3 more|arXiv (Cornell University)|Jul 26, 2016

Robotics and Sensor-Based Localization参考文献 19被引用 3

一句话总结

本文提出了一种基于强化学习（RL）的规划器，即SLAM-Safe Planner，通过学习安全的相机运动策略来生成轨迹，从而防止单目SLAM失败。该RL智能体通过感知输入和运动动作进行训练，生成的轨迹能够保持SLAM的鲁棒性，在模拟环境中相比基线方法显著提升了地图质量和轨迹精度。

ABSTRACT

Effective SLAM using a single monocular camera is highly preferred due to its simplicity. However, when compared to trajectory planning methods using depth-based SLAM, Monocular SLAM in loop does need additional considerations. One main reason being that for the optimization, in the form of Bundle Adjustment (BA), to be robust, the SLAM system needs to scan the area for a reasonable duration. Most monocular SLAM systems do not tolerate large camera rotations between successive views and tend to breakdown. Other reasons for Monocular SLAM failure include ambiguities in decomposition of the Essential Matrix, feature-sparse scenes and more layers of non linear optimization apart from BA. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs (scene structure and camera motion) do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between motor actions and perceptual inputs that result in trajectories that do not cause failure of SLAM, which are almost intractable to capture in an obvious mathematical formulation. We show systematically in simulations how the quality of the SLAM map and trajectory dramatically improves when trajectories are computed by using RL.

研究动机与目标

• 为解决在大角度相机旋转和特征稀疏条件下单目SLAM的不稳定性问题。
• 减少由本质矩阵分解的模糊性和捆绑调整初始化不良导致的SLAM失败。
• 开发一种轨迹规划器，确保感知一致性与SLAM鲁棒性，而无需依赖失败条件的显式数学表达式。
• 通过学习的安全运动策略，提升单目SLAM在回环检测场景下的可靠性。

提出的方法

• 该方法将轨迹规划建模为强化学习问题，以学习安全的相机运动策略。
• RL智能体接收感知输入（如视觉特征、运动估计）并选择动作（相机运动指令），以最小化SLAM误差。
• 奖励函数被设计为惩罚SLAM输出（结构与运动）与真实值之间的大偏差，以促进稳定性。
• 该框架在模拟环境中进行训练，环境包含具有挑战性的单目SLAM条件，如特征稀疏和大角度旋转。
• 使用深度强化学习算法优化策略，将视觉观测映射为保持SLAM完整性的动作。
• 该方法通过在奖励函数中学习失败信号，避免了对SLAM失败模式的显式建模。

实验结果

研究问题

• RQ1 强化学习智能体能否学会生成在复杂环境中防止单目SLAM失败的轨迹？
• RQ2 与传统轨迹规划器相比，基于RL的规划器在保持SLAM精度和鲁棒性方面表现如何？
• RQ3 在未显式建模失败机制的情况下，RL在多大程度上能学习到相机运动与SLAM稳定性之间的隐含关系？
• RQ4 该RL策略在模拟环境中对多样化场景结构和运动轨迹是否具备泛化能力？

主要发现

• SLAM-Safe Planner在模拟环境中显著提升了重建场景结构和相机轨迹的质量。
• 与基线方法相比，RL规划器生成的轨迹在大角度旋转和特征稀疏条件下表现出更低的SLAM误差。
• 即使未显式建模失败机制，RL智能体仍成功学习避免引发SLAM失败的相机运动。
• 通过学习安全的运动模式，该方法在本质矩阵分解模糊性和捆绑调整收敛性差的情况下表现出鲁棒性。
• 地图质量的提升具有可量化的测量结果，表现为漂移减少和结构估计更一致。