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Monocular Street View Localization

Dependencies

While not all modules require the same dependencies, the following is required to run the complete project:

  • Python Version >= 3.7
  • The Python modules from PyPi listed in requirements.txt. Run pip install -r requirments.txt to install.
  • A fork of openMVG found here. This is required to use the kVLD filtering and requires compilation of openMVG. Instructions on how to do so are listed here and vary based on your system. Once compiled, you must change OPENMVG_SFM_BIN in config.py to point to your build directory.

Once the required dependencies are installed, you must modify config.py to correspond to the desired paths for your system. All modules paths defined in config.py to know where to load/save data. Specifically, data_dir should contain a subfolder images with the downloaded panoramas. These can be retrieved by setting the API_KEY in config.py and running python3 download_panoramas.py. Next, you must set recording_dir to the directory containing the recorded video files and metadata.

We used ios_logger to record camera frames and IMU data for offline testing. This program is not dependent on ios_logger but the preprocess_datalog.py script expects data in the format described in the ios_logger repo. preprocess_datalog.py processes these assorted text files and saves a binary file that simplifies later processing.

To run the program:

To download panoramas: python3 download_panoramas.py

To ingest data: python3 preprocess_datalog.py

To produce location estimates: python3 test_localization.py

Overview

Below is a system diagram depicting the primary steps of our pipeline. The paper contains detailed descriptions of the important steps but this section will go over the program architecture, i.e. how they actually run.

system

We first retrieve tiled panoramas from Google Street View by creating a route consisting of lat/long pairs stored in waypoints.py. We treat this as a piecewise linear trajectory and interpolate every n meters in download_panoramas.py. These are saved with their associated metadata (location, pano id, depth data, etc.) in the data dir folder. This script (as well as the rest of the project) was designed to be idempotent and resumable. In other words, you can run the program multiple times with no additional effect, or run them partially and resume where you left off, saving prior computations.

preprocess_datalog.py takes a folder with a video and associated data files from the phone (IMU, GPS, etc.) and processes the text files into a single binary file for easier parsing. This makes it easier to get the data at any given frame and tie all the data together.

Next, we run test_localization.py which does the following at each frame:

  • Finds the nearest n GSV panoramas, backprojects onto a sphere and then creates a virtual image from the perspective of the phone camera
  • Runs feature matching in localization/kvld.py by calling openMVG. This is done by calling a compiled openMVG binary with the correct arguments and parsing the stdout. This performs SIFT, FLANN matching, and kVLD filtering
  • Minimizes the reprojection error using a fixed nonlinear solver (SciPy) or through pose graph optimization with g2o or ceres