MSC-VO

Authors: Joan P. Company-Corcoles, Emilio Garcia-Fidalgo and Alberto Ortiz

Related Publications:

Exploiting Manhattan and Structural Constraints for Visual Odometry. [UnderReview]

General considerations:

MSC-VO is a novel RGB-D visual odometer that increases the performance of the traditional point-based methods by combining points and lines as visual features. Furthermore, to increase the performance in low-textured environments, it leverages structural constraints and Manhattan Axes (MA) alignment in the local map optimization stage.

1. License

MSC-VO is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see [Dependencies_MSC-VO.md].

2. Prerequisites

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. Tested with 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.

DBoW2 and g2o (Included in Thirdparty folder)

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.

3. RGB-D Example

MSC-VO has been evaluated in the ICL-NUIM and the TUM datasets. Furthermore, it has been executed in custom datasets where images are obtained by a RealSense D435.

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.bin Examples/RGB-D/DATASET_CONFIG.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE BOOL_USE_VIEWER

TUM Dataset

1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.

2. Associate RGB images and depth images using the python script associate.py. We already provide associations for some of the sequences in Examples/RGB-D/associations/. You can generate your own associations file executing:

python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt

3. Execute the following command. Change TUMX.yaml to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change PATH_TO_SEQUENCE_FOLDERto the uncompressed sequence folder. Change ASSOCIATIONS_FILE to the path to the corresponding associations file.

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.bin Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE BOOL_USE_VIEWER