Amos-SLAM: An Anti-Dynamics Two-stage SLAM Approach

TL;DR

Amos-SLAM introduces a two-stage geometry-based approach that effectively detects and removes dynamic objects, improving localization accuracy in dynamic environments compared to existing methods.

Contribution

This paper presents a novel anti-dynamics two-stage SLAM method combining motion region extraction and dynamic point removal, addressing limitations of prior dynamic SLAM approaches.

Findings

Outperforms state-of-the-art dynamic SLAM methods on public RGB-D datasets.

Effectively detects both prior and non-prior dynamic objects using geometric and color cues.

Improves localization accuracy in environments with moving objects.

Abstract

The traditional Simultaneous Localization And Mapping (SLAM) systems rely on the assumption of a static environment and fail to accurately estimate the system's location when dynamic objects are present in the background. While learning-based dynamic SLAM systems have difficulties in handling unknown moving objects, geometry-based methods have limited success in addressing the residual effects of unidentified dynamic objects on location estimation. To address these issues, we propose an anti-dynamics two-stage SLAM approach. Firstly, the potential motion regions of both prior and non-prior dynamic objects are extracted and pose estimates for dynamic discrimination are quickly obtained using optical flow tracking and model generation methods. Secondly, dynamic points in each frame are removed through dynamic judgment. For non-prior dynamic objects, we present a approach that uses super-pixel extraction and geometric clustering to determine the potential motion regions based on color and geometric information in the image. Evaluations on multiple low and high dynamic sequences in a public RGB-D dataset show that our proposed method outperforms state-of-the-art dynamic SLAM methods.