Neural Ranging Inertial Odometry

Ultra-wideband (UWB) has shown promising potential in GPS-denied localization thanks to its lightweight and drift-free characteristics, while the accuracy is limited in real scenarios due to its sensitivity to sensor arrangement and non-Gaussian pattern induced by multi-path or multi-signal interference, which commonly occurs in many typical applications like long tunnels. We introduce a novel neural fusion framework for ranging inertial odometry which involves a graph attention UWB network and a recurrent neural inertial network. Our graph net learns scene-relevant ranging patterns and adapts to any number of anchors or tags, realizing accurate positioning without calibration. Additionally, the integration of least squares and the incorporation of nominal frame enhance overall performance and scalability. The effectiveness and robustness of our methods are validated through extensive experiments on both public and self-collected datasets, spanning indoor, outdoor, and tunnel environments. The results demonstrate the superiority of our proposed IR-ULSG in handling challenging conditions, including scenarios outside the convex envelope and cases where only a single anchor is available.

Authors

• Si Wang
• Bingqi Shen
• Fei Wang
• Yanjun Cao
• Rong Xiong
• Yue Wang 0020

Keywords

• Accuracy
• Sensitivity
• Scalability
• Estimation
• Distance measurement
• Robustness
• Odometry
• Channel impulse response
• Robotics and automation
• Ultra wideband technology
• Ranging
• Inertial Odometry
• Neural Network
• Recurrent Neural Network
• Indoor Environments
• Neural Framework
• Convex Envelope
• Least-squares
• Kalman Filter
• Base Station
• Motion Capture
• Fusion Method
• Hidden State
• Submodule
• Particle Filter
• Graph Neural Networks
• Raw Measurements
• Extended Kalman Filter
• Robot Motion
• Least Squares Solution
• World Frame
• Unscented Kalman Filter
• Pose Of Frame
• Pose Prediction
• Robot Pose
• Body Frame
• Radio Waves
• Inertial Confinement Fusion
• Node Features