Multi-modal Semantic SLAM for Complex Dynamic Environments

TL;DR

This paper introduces a robust multi-modal semantic SLAM framework that effectively handles dynamic environments by improving object recognition and combining geometric and semantic data, achieving real-time dense mapping.

Contribution

The paper presents a novel multi-modal semantic SLAM approach with enhanced object feature learning and a dual recognition mechanism, improving performance in dynamic, complex scenes.

Findings

Accurately identifies dynamic objects despite segmentation imperfections.

Achieves dense static mapping at over 10 Hz processing rate.

Effectively combines geometric and semantic information to reduce segmentation errors.

Abstract

Simultaneous Localization and Mapping (SLAM) is one of the most essential techniques in many real-world robotic applications. The assumption of static environments is common in most SLAM algorithms, which however, is not the case for most applications. Recent work on semantic SLAM aims to understand the objects in an environment and distinguish dynamic information from a scene context by performing image-based segmentation. However, the segmentation results are often imperfect or incomplete, which can subsequently reduce the quality of mapping and the accuracy of localization. In this paper, we present a robust multi-modal semantic framework to solve the SLAM problem in complex and highly dynamic environments. We propose to learn a more powerful object feature representation and deploy the mechanism of looking and thinking twice to the backbone network, which leads to a better recognition result to our baseline instance segmentation model. Moreover, both geometric-only clustering and visual semantic information are combined to reduce the effect of segmentation error due to small-scale objects, occlusion and motion blur. Thorough experiments have been conducted to evaluate the performance of the proposed method. The results show that our method can precisely identify dynamic objects under recognition imperfection and motion blur. Moreover, the proposed SLAM framework is able to efficiently build a static dense map at a processing rate of more than 10 Hz, which can be implemented in many practical applications. Both training data and the proposed method is open sourced at https://github.com/wh200720041/MMS_SLAM.