HKUST Aerial Robotics Group

Event-based stereo visual odometry

Check out our new work: "Event-based Stereo Visual Odometry", where we dive into the rather unexplored topic of stereo SLAM with event cameras and propose a real-time solution.

Authors: Yi Zhou, Guillermo Gallego and Shaojie Shen

Code: https://github.com/HKUST-Aerial-Robotics/ESVO

Project webpage: https://sites.google.com/view/esvo-project-page/home
Paper: https://arxiv.org/pdf/2007.15548.pdf

Quadrotor fast flight in complex unknown environments

We presented RAPTOR, a Robust And Perception-aware TrajectOry Replanning framework to enable fast and safe flight in complex unknown environments. Its main features are:

(a) finding feasible and high-quality trajectories in very limited computation time, and

(b) introducing a perception-aware strategy to actively observe and avoid unknown obstacles.

Specifically, a path-guided optimization (PGO) approach that incorporates multiple topological paths is devised to search the solution space efficiently and thoroughly. Trajectories are further refined to have higher visibility and sufficient reaction distance to unknown dangerous regions, while the yaw angle is planned to actively explore the surrounding space relevant for safe navigation.

Authors: Boyu Zhou, Jie Pan, Fei Gao and Shaojie Shen

Code: https://github.com/HKUST-Aerial-Robotics/Fast-Planner

Code for autonomous drone race is now available on GitHub

We released Teach-Repeat-Replan, which is a complete and robust system enables Autonomous Drone Race.

Teach-Repeat-Replan can be applied to situations where the user has a preferable rough route but isn't able to pilot the drone ideally, such as drone racing. With our system, the human pilot can virtually control the drone with his/her navie operations, then our system automatically generates a very efficient repeating trajectory and autonomously execute it. During the flight, unexpected collisions are avoided by onboard sensing/replanning. Teach-Repeat-Replan can also be used for normal autonomous navigations. For these applications, a drone can autonomously fly in complex environments using only onboard sensing and planning.

Major components are:

• Planning: flight corridor generation, global spatial-temporal planning, local online re-planning
• Perception: global deformable surfel mapping, local online ESDF mapping
• Localization: global pose graph optimization, local visual-inertial fusion
• Controlling: geometric controller on SE(3)

Authors: Fei Gao, Boyu Zhou, and Shaojie Shen

Videos: Video1, Video2
Code: https://github.com/HKUST-Aerial-Robotics/Teach-Repeat-Replan

Code for VINS-Fusion is now available on GitHub

VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension of VINS-Mono, which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + IMU, even stereo cameras only). We also show a toy example of fusing VINS with GPS. Features:

• multiple sensors support (stereo cameras / mono camera+IMU / stereo cameras+IMU)
• online spatial calibration (transformation between camera and IMU)
• online temporal calibration (time offset between camera and IMU)
• visual loop closure.

We are the TOP open-sourced stereo algorithm on KITTI Odometry Benchmark by 12 Jan. 2019.