A Robust Distributed Odometry for Mobile Robots with Steerable Wheels

Odometry estimation remains a critical challenge for wheeled robots, as reducing its drift directly mitigates dependency on external localization systems. This paper proposes a distributed odometry framework for steerable wheels, named ICF-DO, which is applicable to both Steerable Wheeled Mobile Robots (SWMRs) and cooperative multi-single-wheel robot systems. The proposed method features low computational complexity and reduced drift, while demonstrating strong robustness in communication-restricted scenarios. Additionally, singularity can be processed in a distributed manner in the proposed framework. Experimental validation on a real physical SWMR platform demonstrates the effectiveness and practicality of the proposed method.

Authors

• Wang Xi
• Jiaming Guo
• Chenyang Wang
• Shukun Wu
• Jianping He 0001

Keywords

• Location awareness
• Wheels
• Packet loss
• Predictive models
• Robot sensing systems
• Robustness
• Real-time systems
• Odometry
• Mobile robots
• Intelligent robots
• Robotic System
• Wheeled Robot
• Root Mean Square Error
• Mean Square Error
• Actuator
• Prior Information
• Approximate Distribution
• Kalman Filter
• Practical Scenarios
• Control Period
• Fisher Information
• Auxiliary Variables
• Distributed Computing
• Real-world Experiments
• Velocity Estimation
• Drift Rate
• Prior Estimates
• Inverse Kinematics
• Communication Topology
• Steering Angle
• Average Consensus
• ICF Framework