A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarm
Omni-swarm is a decentralized omnidirectional visual-inertial-UWB state estimation system for the aerial swarm. In order to solve the issues of observability, complicated initialization, insufficient accuracy and lack of global consistency, we introduce an omnidirectional perception system as the front-end of the Omni-swarm, consisting of omnidirectional sensors, which includes stereo fisheye cameras and ultra-wideband (UWB) sensors, and algorithms, which includes fisheye visual inertial odometry (VIO), multi-drone map-based localization and visual object detection. A graph-based optimization and forward propagation working as the back-end of the Omni-swarm to fuse the measurements from the front-end. According to the experiment result, the proposed decentralized state estimation method on the swarm system achieves centimeter-level relative state estimation accuracy while ensuring global consistency. Moreover, supported by the Omni-swarm, inter-drone collision avoidance can be accomplished in a whole decentralized scheme without any external device, demonstrating the potential of Omni-swarm to be the foundation of autonomous aerial swarm flights in different scenarios.
The is the code for Omni-swarm: A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarms. The manuscript has been accepted by IEEE Transactions on Robotics (T-RO), a preprint version is here.
The structure of Omni-swarm is
The fused measurements of Omni-swarm:
The detailed backend structure of state estimation of Omni-swarm:
The Omni-swarm offical support TX2 with Ubuntu 18.04. For those running on other hardware and system setup, converting the models to trt by your own is essential.
Here to download the CNN models for Omni-swarm and extract it to swarm_loop folder.
Here to get the raw and preprocessed offical omni-directional and pinole dataset.
swarm_msgs inf_uwb_ros are compulsory. And swarm_detector if you want to use detector,
First, running the pinhole or fisheye version of VINS-Fisheye (Yes, VINS-Fisheye is pinhole compatiable and is essential for Omni-swarm).
Start map-based localization with
roslaunch swarm_loop nodelet-sfisheye.launch
or pinhole version
roslaunch swarm_loop realsense.launch
Start visual object detector by (not compulsory)
roslaunch swarm_detector detector.launch
Start UWB communication module with (Support NoopLoop UWB module only)
roslaunch localization_proxy uwb_comm.launch start_uwb_node:=true
If you don't have a UWB module, you may start the communication with a self id(start from 1, different on each drone)
roslaunch localization_proxy uwb_comm.launch start_uwb_node:=true enable_uwb:=false self_id:=1
Start state estimation with visualizer by
roslaunch swarm_localization loop-5-drone.launch bag_replay:=true viz:=true enable_distance:=false cgraph_path:=/home/your-name/output/graph.dot
You may enable/disable specific measurement by adding
enable_distance:=false or enable_detection:=false enable_loop:=true
To visualize the real-time estimation result, use viz:=true.
Add bag_replay:=true only when evaluation dataset, when evaluate pre-processed dataset, you may only launch loop-5-drone.launch
Some analysis tools is located in DataAnalysis
To evaluate the program, a recommended way is by using a docker. We provide a runnable docker on docker hub, TAG is xuhao1/swarm2020:pc.
Due to the limitation of the TensorRT engine adopted in the frontend to accelerate the CNNs, the docker is restricted to running with an RTX 3080 or similar graphic card. We are working on migrating an onnxruntime version of frontend for CNNs referencing and a buildable docker file, which will be released very soon.
GPLV3