In static environments, visual simultaneous localization and mapping (V-SLAM) methods achieve remarkable performance. However, moving objects severely affect core modules of such systems like state estimation and loop closure detection. To address this, dynamic SLAM approaches often use semantic information, geometric constraints, or optical flow to mask features associated with dynamic entities. These are limited by various factors such as a dependency on the quality of the underlying method, poor generalization to unknown or unexpected moving objects, and often produce noisy results, e.g. by masking static but movable objects or making use of predefined thresholds. In this paper, to address these trade-offs, we introduce a novel visual SLAM system, DynaPix, based on per-pixel motion probability values. Our approach consists of a new semantic-free probabilistic pixel-wise motion estimation module and an improved pose optimization process. Our per-pixel motion probability estimation combines a novel static background differencing method on both images and optical flows from splatted frames. DynaPix fully integrates those motion probabilities into both map point selection and weighted bundle adjustment within the tracking and optimization modules of ORB-SLAM2. We evaluate DynaPix against ORB-SLAM2 and DynaSLAM on both GRADE and TUM-RGBD datasets, obtaining lower errors and longer trajectory tracking times. We will release both source code and data upon acceptance of this work.

翻译：在静态环境下，视觉同步定位与地图构建方法表现出色。然而，移动物体严重影响了此类系统的核心模块，如状态估计和闭环检测。为解决这一问题，动态SLAM方法通常利用语义信息、几何约束或光流来掩蔽与动态实体相关的特征。但这些方法受限于多种因素，例如依赖底层方法的质量、对未知或意外移动物体的泛化能力较差，且常因掩蔽静态但可移动物体或使用预定义阈值而产生噪声结果。本文中，为应对这些权衡，我们提出了一种新颖的视觉SLAM系统——DynaPix，其基于逐像素运动概率值。我们的方法包括一个新颖的无语义概率逐像素运动估计模块以及改进的位姿优化过程。逐像素运动概率估计结合了一种新颖的静态背景差分方法，对图像和来自帧渲染的光流进行处理。DynaPix将这些运动概率完全集成到ORB-SLAM2的跟踪与优化模块中的地图点选择和加权光束法平差中。我们在GRADE和TUM-RGBD数据集上，将DynaPix与ORB-SLAM2和DynaSLAM进行对比评估，获得了更低的误差和更长的轨迹跟踪时间。本研究被接收后，我们将公开源代码和数据。