Accurate Full-Body Pose Estimation from Sparse Inertial Sensors and Ultra-Wideband Ranging

Główne pojęcia

A novel method for 3D full-body pose estimation that fuses raw inertial measurements with inter-sensor distances estimated from ultra-wideband ranging, enabling accurate and stable tracking from a sparse set of wearable sensors.

Streszczenie

The paper presents Ultra Inertial Poser, a method for 3D full-body pose estimation that leverages both inertial measurements from a sparse set of wearable sensors and inter-sensor distances estimated using ultra-wideband (UWB) ranging.

Key highlights:

• The method uses 6 wearable sensor nodes, each with an inertial measurement unit (IMU) and a UWB radio, to estimate global orientations, accelerations, and inter-sensor distances.
• A graph-based neural network model fuses the 3D states estimated from the IMU signals and the inter-sensor distances to predict the SMPL parameters of body pose and global translation.
• The authors synthesize IMU signals and distance estimates from the AMASS motion capture dataset to train the model, and augment the data with a collision-aware noise model for the UWB measurements.
• The authors introduce a new motion capture dataset, UIP-DB, which contains synchronized IMU, UWB, and optical motion capture data from 10 participants performing 25 different motion types.
• Experiments show that the proposed method outperforms state-of-the-art IMU-based pose estimation methods, reducing position error by 22% and jitter by 97%.

The key innovation is the fusion of inertial measurements and inter-sensor distances estimated from UWB ranging to enable accurate and stable full-body pose estimation from a sparse set of wearable sensors, without requiring proprietary components or stationary infrastructure.

Statystyki

"Our method surpasses the current SOTA methods PIP [Yi et al. 2022] and TIP [Jiang et al. 2022b] in position accuracy (22% lower error) and jitter (97% reduction)."
"The average error is largest for wrist–wrist and head–knees sensor pairs, as the body frequently occludes the line of sight between sensors."

Cytaty

"Our method then fuses these inter-sensor distances with the 3D states estimated from each sensor. Our graph-based machine learning model processes the 3D states and distances to estimate a person's 3D full body pose and translation."
"To train our model, we synthesize inertial measurements and distance estimates from the motion capture database AMASS. For evaluation, we contribute a novel motion dataset of 10 participants who performed 25 motion types, captured by 6 wearable IMU+UWB trackers and an optical motion capture system, totaling 200 minutes of synchronized sensor data (UIP-DB)."

Kluczowe wnioski z

Ultra Inertial Poser: Scalable Motion Capture and Tracking from Sparse Inertial Sensors and Ultra-Wideband Ranging

by Rayan Armani... o arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19541.pdf

Głębsze pytania

How could the proposed method be extended to handle more complex environments and interactions, such as navigating through cluttered spaces or using handheld objects

To handle more complex environments and interactions, such as navigating through cluttered spaces or using handheld objects, the proposed method could be extended in several ways:

Environmental Mapping: Integrate simultaneous localization and mapping (SLAM) techniques to create a map of the environment. This map can help in understanding the surroundings and navigating through cluttered spaces effectively.

Object Detection and Tracking: Incorporate computer vision algorithms to detect and track handheld objects or obstacles in the environment. By combining visual data with sensor inputs, the system can adapt to dynamic interactions.

Fusion with Depth Sensors: Include depth sensors like LiDAR or depth cameras to enhance spatial awareness and improve object detection. Depth information can aid in better understanding the 3D structure of the environment.

Dynamic Obstacle Avoidance: Implement algorithms for real-time obstacle avoidance based on sensor inputs. This can enable the system to react to dynamic changes in the environment and adjust the pose estimation accordingly.

By integrating these enhancements, the method can be adapted to handle more complex environments and interactions effectively.

What other types of sensor modalities, in addition to IMUs and UWB, could be integrated to further improve the robustness and accuracy of the pose estimation

In addition to IMUs and UWB, integrating the following sensor modalities can further improve the robustness and accuracy of pose estimation:

Depth Cameras: Depth cameras like Microsoft Kinect or Intel RealSense can provide detailed 3D information about the environment and aid in better understanding the spatial relationships between objects and the user.

Pressure Sensors: Pressure sensors integrated into wearable devices can help in detecting interactions with surfaces or objects, providing additional context for pose estimation.

Vision-based Systems: Incorporating cameras for visual data can offer complementary information to IMUs and UWB, enabling the system to validate and refine pose estimates based on visual cues.

Inertial Measurement Systems: Including additional sensors like magnetometers or barometers can enhance the accuracy of orientation estimation and compensate for drift in IMU data.

By fusing data from multiple sensor modalities, the system can leverage the strengths of each sensor type to improve overall pose estimation performance.

How could the method be adapted to enable real-time, low-latency pose estimation for applications like virtual reality or robotics control

To enable real-time, low-latency pose estimation for applications like virtual reality or robotics control, the method can be adapted in the following ways:

Optimized Data Processing: Implement efficient data processing algorithms to minimize latency in sensor data fusion and pose estimation calculations. This can involve parallel processing, optimized algorithms, and hardware acceleration.

Predictive Modeling: Utilize predictive modeling techniques to anticipate movements based on historical data, reducing the latency in pose estimation by predicting future poses.

Hardware Acceleration: Employ specialized hardware such as GPUs or FPGAs to accelerate the computation-intensive tasks involved in pose estimation, enabling real-time performance.

Sensor Fusion Optimization: Fine-tune the sensor fusion algorithms to prioritize real-time responsiveness while maintaining accuracy, ensuring that pose estimates are updated rapidly as new sensor data is received.

By focusing on these aspects and optimizing the system for low-latency operation, the method can be adapted to meet the real-time requirements of applications like virtual reality and robotics control.