Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment

TL;DR

This paper introduces a robust stereo visual SLAM system for outdoor environments that leverages 3D quadric landmarks, improving noise robustness and accuracy through novel initialization and data association techniques.

Contribution

It presents a new quadric initialization method and an object-oriented data association approach that enhance robustness and accuracy in outdoor object-oriented SLAM.

Findings

Outperforms state-of-the-art methods in outdoor scenes

Demonstrates robustness to observation noise

Operates in real-time

Abstract

Object-oriented SLAM is a popular technology in autonomous driving and robotics. In this paper, we propose a stereo visual SLAM with a robust quadric landmark representation method. The system consists of four components, including deep learning detection, object-oriented data association, dual quadric landmark initialization and object-based pose optimization. State-of-the-art quadric-based SLAM algorithms always face observation related problems and are sensitive to observation noise, which limits their application in outdoor scenes. To solve this problem, we propose a quadric initialization method based on the decoupling of the quadric parameters method, which improves the robustness to observation noise. The sufficient object data association algorithm and object-oriented optimization with multiple cues enables a highly accurate object pose estimation that is robust to local observations. Experimental results show that the proposed system is more robust to observation noise and significantly outperforms current state-of-the-art methods in outdoor environments. In addition, the proposed system demonstrates real-time performance.