Quantized Self-Supervised Local Feature for Real-Time Robot Indirect VSLAM

Abstract

The indirect visual simultaneous localization and mapping (VSLAM) is widely used in robot localization and navigation, thanks to its potential to achieve high localization accuracy with the local feature observations. However, the existing local features are subject to drift and mismatches under various visual conditions, which causes a degrading in localization accuracy and tracking loss. This article proposes a quantized self-supervised local feature for the indirect VSLAM to handle the environmental interference in robot localization tasks. A joint feature detection and description network is built in a lightweight manner to extract local features in real time. The network is iteratively trained by a self-supervised learning strategy, and the extracted local features are quantized by an orthogonal transformation for efficiency. We utilize frame-wise matching in Hamming space and bundle adjustment to establish a parallel indirect VSLAM. The proposed VSLAM demonstrates outstanding localization accuracy and tracking stability in the evaluation on multiple datasets and robustness in real-world experiments with the Realsense D435 RGB-D sensor. The efficiency experiment on Jetson TX2 indicates that the quantized self-supervised local feature is suitable for feature-based tasks on edge computing platforms.