Tightly-Coupled LiDAR, Inertial, and Kinematic Odometry for Accurate State Estimation of Bipedal Robots

Khái niệm cốt lõi

A tightly-coupled LiDAR-Inertial-Kinematic Odometry (LIKO) system that achieves high-frequency and accurate state estimation for bipedal robots by leveraging LiDAR, inertial, and kinematic measurements.

Tóm tắt

The paper presents a tightly-coupled LiDAR-Inertial-Kinematic Odometry (LIKO) system for accurate state estimation of bipedal robots. The key highlights are:

LIKO uses an iterated extended Kalman filter to tightly fuse LiDAR, inertial, and kinematic measurements for high-frequency (1kHz) and globally accurate state estimation.

It models and estimates the foot contact position, allowing for both position and velocity updates from the kinematic measurements. This improves the velocity estimation compared to previous methods.

The authors collected a dataset of a bipedal robot (BHR-B3) with LiDAR, IMU, joint encoders, force/torque sensors, and motion capture ground truth. Experiments show LIKO outperforms state-of-the-art LiDAR-inertial and inertial-kinematic odometry methods by 14% in accuracy.

The ablation study demonstrates the individual contributions of different sensor modalities. LiDAR is crucial for global position estimation, while the high-frequency kinematic measurements provide accurate velocity updates.

LIKO's accurate state estimation, including contact position, can benefit downstream control applications for more robust bipedal robot locomotion.

Thống kê

The robot's linear velocity in the X direction reaches up to 0.5 m/s.
The robot's linear velocity in the Y direction reaches up to 0.5 m/s.
The robot's linear velocity in the Z direction reaches up to 0.1 m/s.

Trích dẫn

"High-frequency and accurate state estimation is crucial for biped robots."
"Beyond state estimation, the foot contact position is also modeled and estimated. This allows for both position and velocity updates from kinematic measurement."
"The dataset and source code will be available at https://github.com/Mr-Zqr/LIKO."

Thông tin chi tiết chính được chắt lọc từ

LIKO: LiDAR, Inertial, and Kinematic Odometry for Bipedal Robots

by Qingrui Zhao... lúc arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18047.pdf

Yêu cầu sâu hơn

How could the LIKO system be extended to handle more challenging environments, such as uneven terrain or dynamic obstacles

To extend the LIKO system for more challenging environments like uneven terrain or dynamic obstacles, several enhancements can be implemented. Firstly, integrating additional sensors like 3D cameras or depth sensors can provide detailed environmental information for better localization and mapping. These sensors can help in obstacle detection and avoidance, especially in dynamic environments. Secondly, incorporating advanced algorithms for terrain analysis and adaptive control can assist the robot in adjusting its gait and motion planning based on the terrain topology. Techniques like adaptive foot placement and compliance control can improve stability and performance on uneven surfaces. Moreover, implementing robust localization algorithms that can handle featureless or changing environments, such as SLAM (Simultaneous Localization and Mapping) with loop closure detection, can enhance the system's resilience in challenging scenarios. By combining these sensor modalities and algorithms, the LIKO system can be extended to navigate through complex terrains and dynamic obstacles effectively.

What other sensor modalities, such as vision or tactile feedback, could be integrated into the LIKO framework to further improve state estimation accuracy and robustness

Integrating additional sensor modalities like vision and tactile feedback into the LIKO framework can significantly enhance state estimation accuracy and robustness. Vision sensors, such as cameras or depth sensors, can provide rich environmental information for better localization and mapping. By incorporating visual odometry techniques, the system can improve pose estimation and obstacle detection capabilities. Tactile sensors on the robot's feet can offer valuable feedback on ground contact forces and surface properties, aiding in terrain classification and adaptive locomotion control. Fusion of vision-based perception with LiDAR and IMU data can enhance the system's understanding of the surroundings and improve navigation in complex environments. Furthermore, tactile feedback can enable the robot to adapt its walking pattern based on surface conditions, enhancing stability and performance. By integrating vision and tactile feedback, the LIKO system can achieve more accurate and robust state estimation for bipedal robots.

How could the LIKO system be adapted to work with other types of legged robots, such as quadrupeds or hexapods, and what additional challenges would need to be addressed

Adapting the LIKO system to work with other types of legged robots, such as quadrupeds or hexapods, would involve addressing specific challenges related to the different kinematic structures and locomotion patterns of these robots. For quadrupeds, the system would need to account for additional limbs and a different gait pattern compared to bipeds. This would require modifications in the kinematic model and sensor fusion algorithms to accommodate the increased complexity. Integration of additional proprioceptive sensors for each limb and advanced control strategies for coordinating motion among multiple limbs would be essential. In the case of hexapods, the system would need to handle even more legs and a wider range of motion possibilities. This would necessitate a more sophisticated state estimation framework capable of handling the increased degrees of freedom and redundancy in motion. Adapting LIKO for quadrupeds or hexapods would also involve fine-tuning the foot contact estimation and motion planning algorithms to suit the specific locomotion characteristics of these robots. By addressing these challenges and tailoring the system to the unique requirements of quadrupeds or hexapods, LIKO can be successfully adapted to work with a variety of legged robot platforms.