Tightly-coupled Visual-Inertial-LiDAR SLAM

Since Amazon robotics expanded the use of drones to package deliveries to customers, drone applications have been expanded to many industries along with its ability to perform various tasks autonomously. The fundamental technology of drones’ autonomy comes from perceiving its surrounding, creating its own map based on onboard sensors and estimate its location within the map.

Guidance and Control of Hybrid Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) with Thrust Vectoring Capabilities

Quadrotors are one of the most popular choices for unmanned aerial vehicles (UAVs) in situations where fast disturbance rejection, vertical takeoff and landing (VTOL) capabilities, and maneuverability are required. However, the quadrotor is inherently underactuated, and as a result, it is impossible to independently control the orientation and position of the vehicle. One solution to this problem involves rotors that can rotate relative to the vehicle frame, allowing for the angle of each rotor relative to the main vehicle frame to be independently controlled.

Weighted linear point-cloud registration for scan-matching in GPS-denied environments

The ability for an autonomous robot to create its own map based on onboard sensors and simultaneously, localize itself within this map is know as Simultaneous Localization and Mapping (SLAM). Although the theory behind SLAM has been well developed much work still needs to be done in realizing SLAM solutions that meet situation-specific real-world requirements. This is because sensors and actuators onboard a robot are always corrupted by noise. In particular, Unmanned Aerial Vehicles (UAVs), which travel in 3D, face additional difficulties due to the nonlinearities associated with rotation.

3D RFM SLAM for UAVs Subject to Severe Vibration

The first step for any robot to achieve true autonomy is to create a map of its surroundings and localize itself within this map at the same time. This is popularly known as the Simultaneous Localization and Mapping (SLAM) problem. Although much theory has been developed over the years to solve the SLAM problem, researchers have been having difficulties in real-world application. This is because sensors and actuators onboard a robot are always corrupted by noise. In particular, Unmanned Aerial Vehicles (UAVs) face additional difficulties that land vehicles do not.

Development of a precise and robust INS/GPS navigation system using low cost MEMS sensors dedicated to autonomous multirotor applications

Unmanned Aerial Vehicles (UAVs) became increasingly more popular since the global industry realized the unlimited possible applications assignable to these vehicles for reasonable costs. In this way, the company ARA Robotique designs flight controllers for multi-rotors UAVs that need accurate positions, velocities and attitude (roll, pitch, yaw) knowledge. This project focuses on designing a low-cost inertial navigation system (INS) suitable for aerial navigation.

Development of a precise and robust INS/GPS navigation system using low cost MEMS sensors dedicated to autonomous multirotor applications

ARA Robotique is a company specialized in the development of a state-of-art flight controller for light multirotor UAV. One of the critical subsystems of a flight controller is its navigation system which measures the position and the orientation of the vehicle which is then used to ensure the flight stability and to operate the UAV. To complete its flight controller design, ARA Robotique is interested in developing a robust and accurate Inertial Navigation System (INS) based on low cost Microelectromechanical system (MEMS) technology.