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

arXiv: https://arxiv.org/abs/2110.08977 · HTML: https://mlsanity.com/paper/2110.08977/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM](https://mlsanity.com/paper/1804.04011/) (2018)
- [EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association](https://mlsanity.com/paper/2004.12730/) (2020)
- [Monocular Object and Plane SLAM in Structured Environments](https://mlsanity.com/paper/1809.03415/) (2018)
- [Real-Time Monocular Object-Model Aware Sparse SLAM](https://mlsanity.com/paper/1809.09149/) (2018)
- [Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry](https://mlsanity.com/paper/2101.05995/) (2021)
- [Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments](https://mlsanity.com/paper/2004.05303/) (2020)
- [Semantic SLAM with Autonomous Object-Level Data Association](https://mlsanity.com/paper/2011.10625/) (2020)

## Built on by (most cited first, 1 of 1)

- [Perspective-1-Ellipsoid: Formulation, Analysis and Solutions of the Camera Pose Estimation Problem from One Ellipse-Ellipsoid Correspondence](https://mlsanity.com/paper/2208.12513/) (2022)

## Abstract (arXiv)

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.