Mobile Robot Localization and Odometry Calibration
Summary
Mobile robot localization and odometry calibration constitute the backbone of autonomous navigation in ground-based robotic systems. Localization denotes the process by which a robot determines its position and orientation within an environment, while odometry involves estimating incremental motion by integrating wheel encoder readings or inertial measurements over time. Uncorrected odometric data accumulates systematic and non-systematic errors—arising from wheel slippage, uneven surfaces, sensor noise and mechanical tolerances—leading to substantial drift. Modern approaches mitigate this drift through sensor fusion architectures that blend odometry with complementary sources such as inertial measurement units, LiDAR, vision and ultra-wideband ranging. Simultaneous Localization and Mapping (SLAM) frameworks further refine position estimates by constructing and updating a map of the surroundings. Calibration of odometry parameters—whether via offline batch methods, online recursive filters or evolutionary algorithms—remains essential to align the mathematical motion model with the physical realities of the platform. By quantifying and compensating for kinematic inaccuracies and environmental disturbances, well-calibrated odometry underpins reliable path following, precise docking and complex multi-robot coordination in applications ranging from warehouse automation to planetary exploration.
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Mobile Robot Localization and Odometry Calibration publication trend
The graph below shows the total number of articles in mobile robot localization and odometry calibration across all publications each year (not limited to Nature Index journals).
Technical terms
Odometry: Estimation of a robot’s relative displacement over time by integrating wheel encoder or inertial measurements.
Simultaneous Localization and Mapping (SLAM): Algorithmic framework that constructs a map of the environment while concurrently tracking the robot’s pose within it.
Differential-drive: Locomotion scheme in which two independently driven wheels allow steering and forward motion by varying their relative velocities.
Omnidirectional robot: Mobile platform equipped with specialised wheels or mechanisms enabling movement in any horizontal direction without reorientation.
Kinematic parameters: Geometric and mechanical coefficients—such as wheel radii and axle separation—that define the motion model of a robot.
Extended Kalman Filter (EKF): Recursive estimator that linearises nonlinear motion and measurement models to fuse multi-sensor data for state estimation.
References
- Online Odometry Calibration for Differential Drive Mobile Robots in Low Traction Conditions with Slippage. Robotics (2023).
- Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories. Applied Sciences (2022).
- Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms. Applied Sciences (2023).
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