Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames

TL;DR

This paper introduces a real-time monocular SLAM system enhanced with deep learning-based semantic segmentation, improving localization accuracy by addressing scale estimation and dynamic object mapping challenges efficiently.

Contribution

It presents a novel approach that selectively applies semantic segmentation to downsampled keyframes and corrects scales using ground plane estimation, enabling real-time performance on standard hardware.

Findings

Achieves significantly improved trajectory accuracy.

Operates in real-time on standard CPU and GPU.

Effectively removes dynamic objects and low-parallax areas.

Abstract

Monocular simultaneous localization and mapping (SLAM) is emerging in advanced driver assistance systems and autonomous driving, because a single camera is cheap and easy to install. Conventional monocular SLAM has two major challenges leading inaccurate localization and mapping. First, it is challenging to estimate scales in localization and mapping. Second, conventional monocular SLAM uses inappropriate mapping factors such as dynamic objects and low-parallax areas in mapping. This paper proposes an improved real-time monocular SLAM that resolves the aforementioned challenges by efficiently using deep learning-based semantic segmentation. To achieve the real-time execution of the proposed method, we apply semantic segmentation only to downsampled keyframes in parallel with mapping processes. In addition, the proposed method corrects scales of camera poses and three-dimensional (3D) points, using estimated ground plane from road-labeled 3D points and the real camera height. The proposed method also removes inappropriate corner features labeled as moving objects and low parallax areas. Experiments with eight video sequences demonstrate that the proposed monocular SLAM system achieves significantly improved and comparable trajectory tracking accuracy, compared to existing state-of-the-art monocular and stereo SLAM systems, respectively. The proposed system can achieve real-time tracking on a standard CPU potentially with a standard GPU support, whereas existing segmentation-aided monocular SLAM does not.