Visual (LiDAR/camera-based) SLAM systems deploy compute and memory-intensive search algorithms to detect "Loop Closures" to make the trajectory estimate globally consistent. Instead, WAIS (WiFi Assisted Indoor SLAM) demonstrates using WiFi-based sensing can reduce this resource intensiveness drastically.
By covering over 1500 m in realistic indoor environments and WiFi deployments, we showcase 4.3 x and 4 x reduction in compute and memory consumption compared to state-of-the-art Visual and Lidar SLAM systems. Incorporating WiFi into the sensor stack also improves the resiliency of the Visual-SLAM system. We find the 90th percentile translation errors improve by ~40% and orientation errors by ~60% compared with purely camera-based systems.
The following video showcases WAIS's performance on the dataset provided below. You may use the code here to recreate this demo as a starting point.
This project directory contains four primary folders:
- python: Helper code to extract path data collected in bag files
- rviz: RVIZ config file to visualize the path, AP positions, memory and compute trends.
- launch: Use wais.launch contained inside to run the provided dataset
- src: Contains the packages to run WAIS and monitor the memory and CPU information.
- cpu_monitor: Publish CPU and RAM memory usage to individual topics. More information here.
- graph_rviz_plugin: QT plugin to plot lines graphs in RVIZ. More information here.
- python-node: Helper codes to process P2SLAM datasets, compute relative odometry, and plot path within python.
- rf_msgs: A clone of rf_msgs message type from WiROS package.
- wifi_backend: Main package which contains the code to run WAIS's WiFi factor graph.
To install these packages, clone this repository and in the repository's root folder run catkin build:
cd /home/user/
git clone https://github.com/ucsdwcsng/WAIS.git
cd WAIS
catkin build
Source the packages and confirm the paths are included:
source ./devel/setup.bash
echo $ROS_PACKAGE_PATH
Download the dataset following the guidelines provided below and change the bag path param name in the launch file.
<!-- Download the dataset and add the path of the bag file here -->
<arg name="bag_path" default = "path/to/dataset bag file" />
Run the launch file. This should open a termux window to play the bag file and an rviz window to display the data. Note the bag file will take a few minutes to open for the first time:
roslaunch ./launch/wais.launch
Note: This version of WAIS compiles with GTSAM commit id d3544d4c06. Please checkout this commit and install it.
We provided the WAIS dataset to test this code. The dataset can be downloaded by agreeing to the terms and conditions and filling up this form. You will receive instructions to download the dataset via the provided email.
The dataset is a rosbag with the following data:
path: 7-22.bag
version: 2.0
duration: 23:43s (1423s)
start: Jul 21 2022 18:17:57.96 (1658452677.96)
end: Jul 21 2022 18:41:41.07 (1658454101.07)
size: 6.0 GB
messages: 1311099
compression: none [8011/8011 chunks]
types: kobuki_msgs/SensorState [430a4bfd78449c8740bfef32b2661]
nav_msgs/Odometry [cd5e73d190d741a2f92e81eda573a]
realsense2_camera/IMUInfo [a02adb3a99530b11ba18a16f40f95]
rf_msgs/Aoa [967f0be24bd6e9e8f5c88973721bd]
rf_msgs/Wifi [c6b3816e58324f4c826cc5e2cd4f2f]
sensor_msgs/CameraInfo [c9a58c1b0b154e0e6da7578cb991d2]
sensor_msgs/CompressedImage [8f7a12909da2c9d3332d540a097756]
sensor_msgs/Imu [6a62c6daae103f4ff57a132d6f95ce]
sensor_msgs/LaserScan [90c7ef2dc6895d81024acba2ac42f36]
std_msgs/Float32 [73fcbf46b49191e672908e50842a83d]
std_msgs/UInt64 [1b2a79973e8bf53d7b53acb71299cb5]
tf2_msgs/TFMessage [94810edda583a504dfda3829e70d7ee]
topics: /aoa 14634 msgs : rf_msgs/Aoa
/camera/accel/imu_info 1 msg : realsense2_camera/IMUInfo
/camera/color/camera_info 42669 msgs : sensor_msgs/CameraInfo
/camera/color/image_raw/compressed 42669 msgs : sensor_msgs/CompressedImag
/camera/depth/camera_info 42678 msgs : sensor_msgs/CameraInfo
/camera/depth/image_rect_raw/compressed 42677 msgs : sensor_msgs/CompressedImag
/camera/gyro/imu_info 1 msg : realsense2_camera/IMUInfo
/camera/imu 270369 msgs : sensor_msgs/Imu
/camera/infra1/camera_info 42678 msgs : sensor_msgs/CameraInfo
/camera/infra1/image_rect_raw/compressed 42678 msgs : sensor_msgs/CompressedImag
/camera/infra2/camera_info 42678 msgs : sensor_msgs/CameraInfo
/camera/infra2/image_rect_raw/compressed 42678 msgs : sensor_msgs/CompressedImag
/cpu_monitor/aoa_node/cpu 1176 msgs : std_msgs/Float32
/cpu_monitor/aoa_node/mem 1176 msgs : std_msgs/UInt64
/csi 18061 msgs : rf_msgs/Wifi
/mobile_base/sensors/core 71121 msgs : kobuki_msgs/SensorState
/mobile_base/sensors/imu_data 71121 msgs : sensor_msgs/Imu
/mobile_base/sensors/imu_data_raw 154834 msgs : sensor_msgs/Imu
/odom 71119 msgs : nav_msgs/Odometry
/scan 56490 msgs : sensor_msgs/LaserScan
/tf 239589 msgs : tf2_msgs/TFMessage
/tf_static
This work also open-sources the WiROS platform, which allows researchers to conveniently collect WiFi CSI information and process this information to extract AoA and ToF of the received signal. More details can be found here.
If you have found this work useful, please cite us as following:
@inproceedings{arun2024wais,
title={WAIS: Leveraging WiFi for Resource-Efficient SLAM},
author={Arun, Aditya and Hunter, William and Ayyalasomayajula, Roshan and Bharadia, Dinesh},
booktitle={Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services},
pages={561--574},
year={2024}
}