Choosing the Optimal 3D Planner for UAV Autonomous Exploration in Cluttered Environments

Hi there!
First, thank you for the great work and for opening it up for the community.

I'm currently exploring different UAV 3D planners for use in autonomous exploration in cluttered environments. I've come across several planners like Faster, Mader, FastPlanner (https://github.com/HKUST-Aerial-Robotics/Fast-Planner), FUEL (https://github.com/HKUST-Aerial-Robotics/FUEL), and EGO-Planner or EGO Swarm (https://github.com/ZJU-FAST-Lab/ego-planner-swarm). Each of these has its implementation and approach, yet they share a similar goal in terms of the overall objective.

I'm interested in understanding the key differences between these planners. If you had to choose one for an autonomous exploration task in a cluttered environment, which one would it be and why? Are there other planners, perhaps newer or less known, that you would recommend for such applications?

Additionally, I'm curious about the integration of depth sensing in these systems. RGB-D cameras like the Realsense series are commonly used, but what about the potential of LIDAR or time-of-flight (TOF) sensors for more accurate SLAM? For example, the TOF sensors and their lightweight nature could offer extended flight times, which seems particularly beneficial.

Any insights or recommendations you have would be incredibly helpful!

Thanks a lot!

The issue mentions Faster and several external UAV planners, but it names no repository files, tests, or entry points. Begin by reviewing the planner's existing documentation and architecture, then determine whether a concrete comparison or integration guidance can be defined as the completed scope.