VOOM is a real-time visual SLAM library that uses high-level objects and low-level points as hierarchical landmarks in a coarse-to-fine manner. It computes the camera trajectory and a sparse 3D reconstruction.

This work has been accepted by ICRA 2024 🎉 [pdf] [video].

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 our visual object odometry backend, the updated map is employed to further optimize the camera pose and the objects. At the same time, local bundle adjustment is performed utilizing the objects and points-based covisibility graphs in our visual object mapping process. Our experiments demonstrate that the localization accuracy of the proposed VOOM not only exceeds that of other object-oriented SLAM but also surpasses that of feature points SLAM systems such as ORB-SLAM2. The videos of the results can be found at: https://www.bilibili.com/video/BV1w14y1C7Jb/ .

• Pangolin We use Pangolin for visualization and user interface. Download and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.
• OpenCV We use OpenCV to manipulate images and features. Download and install instructions can be found at: http://opencv.org. Required at least 2.4.3. Tested with OpenCV 2.4.11 and OpenCV 3.2.
• Eigen3 Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.
• Dlib We use Dlib in the comparison object data association method used by OA-SLAM. Download and install instructions can be found at: https://github.com/davisking/dlib
• Protocol Buffers It is used for Osmap (see below). Download and install instructions can be found at: https://github.com/protocolbuffers/protobuf

• DBoW2 and g2o We use modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.
• Json for I/O json files.
• Osmap for map saving/loading. Modified version to handle objects.

1. Clone the repository recursively:

   git clone https://github.com/yutongwangBIT/VOOM.git VOOM

2. Build:

   sh build.sh

1. TUM RGBD
2. LM Data Diamond sequences

Our system takes instance segmentation as input. We provide detections in JSON files in the Data folder. We used an off-the-shelf version of YOLOv8, the Python script to prepare the JSON file is in the PythonScripts folder. The camera parameters are available in the Cameras folder.

All command lines can be found in https://github.com/yutongwangBIT/VOOM/blob/main/script

An example usage on TUM Fr2_desk sequence:

cd bin/
./rgbd_tum_with_ellipse ../Vocabulary/ORBvoc.txt ../Cameras/TUM2.yaml PATH_TO_DATASET ../Data/fr2_desk/fr2_desk.txt ../Data/fr2_desk/detections_yolov8x_seg_tum_rgbd_fr2_desk_with_ellipse.json points fr2_desk

VOOM is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md.

If you use VOOM in an academic work, please cite our paper:

@inproceedings{wang2024icra,
	author = {Yutong Wang and Chaoyang Jiang and Xieyuanli Chen},
	title = {{VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks}},
	booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
	year = 2024
}