Abstract
Visual simultaneous localization and mapping (v-SLAM) and navigation of unmanned aerial vehicles (UAVs) are receiving increasing attention in both research and education. However, extensive physical testing can be expensive and time-consuming due to safety precautions, battery constraints, and the complexity of hardware setups. For the efficient development of navigation algorithms and autonomous systems, as well as for education purposes, the ROS-Gazebo-PX4 simulator was customized in-depth, integrated into our previous released research works, and provided as an end-to-end simulation (E2ES) solution for UAV, v-SLAM, and navigation applications. Unlike most other similar works, which can only stimulate certain parts of the navigation algorithms, the E2ES platform simulates all of the localization, mapping, and path-planning kits in one simulator. The navigation stack performs well in the E2ES test bench with the absolute pose errors of 0.3 m (translation) and 0.9 degree (rotation), respectively, for an 83 m length trajectory. Moreover, the E2ES provides an out-of-box, click-and-fly autonomy in UAV navigation. The project source code is opened for the benefit of the research community.
Highlights
With the advent of modern artificial intelligence algorithms, multi-rotor unmanned aerial vehicles (UAVs) have become smart agents that can navigate in unknown environments
The accuracy of the Visual simultaneous localization and mapping (v-SLAM) localization is presented by the absolute pose error (APE) of translation and rotation [37]
The translation and rotation errors are defined by the root mean square error (RMSE) of APE, using: APEtrans = RMSE(Eape,1:m)
Summary
With the advent of modern artificial intelligence algorithms, multi-rotor unmanned aerial vehicles (UAVs) have become smart agents that can navigate in unknown environments. Based on the widely used ROS-Gazebo-PX4 toolchain, we made several improvements to the UAV model, environment, and function plugins to meet the requirement of UAV v-SLAM and navigation (Figure 2) These improvements include: (a) the construction of a simulated world, (b) the customization of UAV models, (c) the addition of a stereo camera model, and (d) the configuration of a vision-based control setup. Comparing E2ES with XTDrone, both are based on ROS-Gazebo-PX4 toolchains, which means they have a similar kinetic model and flight controller These two projects focus on solving different problems. E2ES is more accessible for achieving full-stack navigation in the loop using its default localization, mapping, and planning tools or customized packages developed by users
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
