visual-odometry-rs

• core:: Core modules for computing gradients, candidate points, camera tracking etc.
• dataset:: Helper modules for handling specific datasets. Currently only provides a module for TUM RGB-D compatible datasets.
• math:: Basic math modules for functionalities not already provided by nalgebra, like Lie algebra for so3, se3, and an iterative optimizer trait.
• misc:: Helper modules for interoperability, visualization, and other things that did not fit elsewhere yet.

Library Usage Examples

Self contained examples for usage of the API are available in the examples/ directory. A readme is also present there for more detailed explanations on these examples.

Functionalities and Vision

Currently, vors provides a visual odometry framework for working on direct RGB-D camera tracking. Setting all this from the ground up took a lot of time and effort, but I think it is mature enough to be shared as is now. Beware, however, that the API is evolving a lot. My hope is that in the near future, we can improve the reach of this project by working both on research extensions, and platform availability.

Example research extensions:

• Using disparity search for depth initialization to be compatible with RGB (no depth) camera.
• Adding a photometric term to the residual to account for automatic exposure variations.
• Adding automatic photometric and/or geometric camera calibration.
• Building a sliding window of keyframes optimization as in DSO to reduce drift.
• Intregrating loop closure and pose graph optimization for having a robust vSLAM system.
• Fusion with IMU for improved tracking and reducing scale drift.
• Modelization of rolling shutter (in most cameras) into the optimization problem.
• Extension to stereo cameras.
• Extension to omnidirectional cameras.

Example platform extensions:

• Making a C FFI to be able to run on systems with C drivers (kinect, realsense, ...).
• Porting to the web with WebAssembly.
• Porting to ARM for running in embedded systems and phones.

Background Story

Initially, this repository served as a personal experimental sandbox for computer vision in Rust. See for example my original questions on the rust discourse and reddit channel. Turns out I struggled a bit at first but then really liked the Rust way, compared to C++.

As the name suggests, the focus is now on visual odometry, specifically on the recent research field of direct visual odometry. A reasonable introduction is available in those lecture slides by Waterloo Autonomous Vehicles lab.

In particular, this project initially aimed at improving on the work of DSO by J. Engel et. al. but with all the advantages of using the Rust programming language, including:

• Performance without sacrificing code readability
• No memory error, and much higher code safety and reliability
• Friendly tooling ecosystem, no dependency issues, basically one-liner compilation and run
• Best tooling for porting to the web with WebAssembly
• Growing and mindful resources for porting to embedded systems
• Wonderful community

License (MPL-2.0)

This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

Contributions

All forms of contribution are welcomed, preferably first as github issues.

• Questions
• Documentation
• Tests
• Benchmarks
• Features

In case of contribution to source code, it needs to use rustfmt and clippy. To run clippy:

touch src/lib.rs; cargo clippy --release --all-targets --all-features