Progress Update 2019.08.01-2019.08.28

What We Have Worked On

Major Bug Fixed

We have fixed a bug related to calculation of SLAM rotation angle. Now the SLAM algorithms allow UAVs to have more stable performance in long-distance flight. We have seen a notable improvement in performance in the simulator.

GAAS now supports AirSim!

We switched to AirSim to collect new image data to test the performance of the algorithms in a more realistic simulation environment. We have also published a new case study 1 to provide a step-by-step configuration of GAAS with AirSim.

Added New Global Optimization Module

We have added a new global optimization module 2 to optimize the position and altitude of UAVs globally. When the flight control or SLAM is being rebooted, the position and altitude information can ensure the stability of position estimation.

What We Are Working On

We are looking into the viability of a new neural network that will potentially improve the performance of existing depth estimation algorithms, which has limited performance.

We are updating the mapping algorithms. Existing Octomap algorithm is one of the representations of Occupancy, but a lot of route navigation algorithms require information other than Occupancy.

We are enhancing the multi-sensor fusion algorithm to improve system tolerance.

What We Will Work On Next

• Improve multi-sensor fusion algorithm 2.

• Use voxblox 1 to replace Octomap algorithm for mapping projection

• Establish dataset based on AirSim to train neural networks to optimize depth estimation.