# VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks

arXiv: https://arxiv.org/abs/2402.13609 · HTML: https://mlsanity.com/paper/2402.13609/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [Fusion++: Volumetric Object-Level SLAM](https://mlsanity.com/paper/1808.08378/) (2018)
- [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://mlsanity.com/paper/1804.09194/) (2018)
- [QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM](https://mlsanity.com/paper/1804.04011/) (2018)
- [A Normalized Gaussian Wasserstein Distance for Tiny Object Detection](https://mlsanity.com/paper/2110.13389/) (2021)
- [Meaningful Maps With Object-Oriented Semantic Mapping](https://mlsanity.com/paper/1609.07849/) (2016)
- [Direct Sparse Mapping](https://mlsanity.com/paper/1904.06577/) (2019)
- [EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association](https://mlsanity.com/paper/2004.12730/) (2020)
- [SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints](https://mlsanity.com/paper/2109.04884/) (2021)
- [OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM](https://mlsanity.com/paper/2209.08338/) (2022)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)

## Built on by (most cited first, 6 of 6)

- [GOReloc: Graph-based Object-Level Relocalization for Visual SLAM](https://mlsanity.com/paper/2408.07917/) (2024)
- [MCOO-SLAM: A Multi-Camera Omnidirectional Object SLAM System](https://mlsanity.com/paper/2506.15402/) (2025)
- [MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization](https://mlsanity.com/paper/2512.03522/) (2025)
- [LangGS-SLAM: Real-Time Language-Feature Gaussian Splatting SLAM](https://mlsanity.com/paper/2602.06991/) (2026)
- [Convex Hull-based Algebraic Constraint for Visual Quadric SLAM](https://mlsanity.com/paper/2503.01254/) (2025)
- [DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting](https://mlsanity.com/paper/2503.02223/) (2025)

## Abstract (arXiv)

In recent years, object-oriented simultaneous localization and mapping (SLAM) has attracted increasing attention due to its ability to provide high-level semantic information while maintaining computational efficiency. Some researchers have attempted to enhance localization accuracy by integrating the modeled object residuals into bundle adjustment. However, few have demonstrated better results than feature-based visual SLAM systems, as the generic coarse object models, such as cuboids or ellipsoids, are less accurate than feature points. In this paper, we propose a Visual Object Odometry and Mapping framework VOOM using high-level objects and low-level points as the hierarchical landmarks in a coarse-to-fine manner instead of directly using object residuals in bundle adjustment. Firstly, we introduce an improved observation model and a novel data association method for dual quadrics, employed to represent physical objects. It facilitates the creation of a 3D map that closely reflects reality. Next, we use object information to enhance the data association of feature points and consequently update the map. In the visual object odometry backend, the updated map is employed to further optimize the camera pose and the objects. Meanwhile, local bundle adjustment is performed utilizing the objects and points-based covisibility graphs in our visual object mapping process. Experiments show that VOOM outperforms both object-oriented SLAM and feature points SLAM systems such as ORB-SLAM2 in terms of localization. The implementation of our method is available at https://github.com/yutongwangBIT/VOOM.git.