Dynamic semantic VSLAM with known and unknown objects

TL;DR

This paper presents a novel semantic VSLAM system that effectively detects dynamic features involving both known and unknown objects using unsupervised segmentation and optical flow, outperforming traditional methods in dynamic environments.

Contribution

Introduces an unsupervised segmentation-based Semantic VSLAM capable of identifying dynamic features for known and unknown objects, enhancing robustness in dynamic scenes.

Findings

Outperforms traditional VSLAM in environments with unknown objects.

Matches state-of-the-art performance with only known objects.

Uses unsupervised segmentation and optical flow for dynamic detection.

Abstract

Traditional Visual Simultaneous Localization and Mapping (VSLAM) systems assume a static environment, which makes them ineffective in highly dynamic settings. To overcome this, many approaches integrate semantic information from deep learning models to identify dynamic regions within images. However, these methods face a significant limitation as a supervised model cannot recognize objects not included in the training datasets. This paper introduces a novel feature-based Semantic VSLAM capable of detecting dynamic features in the presence of both known and unknown objects. By employing an unsupervised segmentation network, we achieve unlabeled segmentation, and next utilize an objector detector to identify any of the known classes among those. We then pair this with the computed high-gradient optical-flow information to next identify the static versus dynamic segmentations for both known and unknown object classes. A consistency check module is also introduced for further refinement and final classification into static versus dynamic features. Evaluations using public datasets demonstrate that our method offers superior performance than traditional VSLAM when unknown objects are present in the images while still matching the performance of the leading semantic VSLAM techniques when the images contain only the known objects