3DS-SLAM: A 3D Object Detection based Semantic SLAM towards Dynamic Indoor Environments

TL;DR

3DS-SLAM introduces a tightly integrated 3D semantic SLAM system that uses 3D object detection and dynamic feature filtering to improve localization accuracy in dynamic indoor environments, outperforming existing methods.

Contribution

The paper presents a novel 3D semantic SLAM approach with a hybrid transformer for object detection and a dynamic feature filter, enhancing performance in dynamic scenes.

Findings

Achieves 98.01% improvement over ORB-SLAM2 in dynamic sequences

Outperforms four leading SLAM systems for dynamic environments

Effectively detects and filters dynamic objects using 3D semantic information

Abstract

The existence of variable factors within the environment can cause a decline in camera localization accuracy, as it violates the fundamental assumption of a static environment in Simultaneous Localization and Mapping (SLAM) algorithms. Recent semantic SLAM systems towards dynamic environments either rely solely on 2D semantic information, or solely on geometric information, or combine their results in a loosely integrated manner. In this research paper, we introduce 3DS-SLAM, 3D Semantic SLAM, tailored for dynamic scenes with visual 3D object detection. The 3DS-SLAM is a tightly-coupled algorithm resolving both semantic and geometric constraints sequentially. We designed a 3D part-aware hybrid transformer for point cloud-based object detection to identify dynamic objects. Subsequently, we propose a dynamic feature filter based on HDBSCAN clustering to extract objects with significant absolute depth differences. When compared against ORB-SLAM2, 3DS-SLAM exhibits an average improvement of 98.01% across the dynamic sequences of the TUM RGB-D dataset. Furthermore, it surpasses the performance of the other four leading SLAM systems designed for dynamic environments.