Accurate parameter estimation of systematic odometry errors for two-wheel differential mobile robots

Odometry using incremental wheel encoder sensors provides the relative robot pose estimation.However,the odometry suffers from the accumulation of kinematic modeling errors of wheels as the robot's travel distance increases.Therefore,the systematic errors need to be calibrated.The University of Michigan Benchmark(UMBmark)method is a widely used calibration scheme of the systematic errors in two-wheel differential mobile robots.In this paper,the accurate parameter estimation of systematic errors is proposed by extending the conventional method.The contributions of this paper can be summarized as two issues.The first contribution is to present new calibration equations that reduce the systematic odometry errors.The new equations were derived to overcome the limitation of conventional schemes.The second contribution is to propose the design guideline of the test track for calibration experiments.The calibration performance can be improved by appropriate design of the test track.The simulations and experimental results show that the accurate parameter estimation can be implemented by the proposed method.

Particle-filter-based walking prediction model for occlusion situations

In the field of mobile robotics,human tracking has emerged as an important objective for facilitating human-robot interaction.In this paper,we propose a particle-filter-based walking prediction model that will address an occlusion situation.Since the target being tracked is a human leg,a motion model for a leg is required.The validity of the proposed model is verified experimentally.

3D obstacle detection of indoor mobile robots by floor detection and rejection

Obstacle detection is essential for mobile robots to avoid collision with obstacles.Mobile robots usually operate in indoor environments,where they encounter various kinds of obstacles;however,2D range sensor can sense obstacles only in 2D plane.In contrast,by using 3D range sensor,it is possible to detect ground and aerial obstacles that 2D range sensor cannot sense.In this paper,we present a 3D obstacle detection method that will help overcome the limitations of 2D range sensor with regard to obstacle detection.The indoor environment typically consists of a flat floor.The position of the floor can be determined by estimating the plane using the least squares method.Having determined the position of the floor,the points of obstacles can be known by rejecting the points of the floor.In the experimental section,we show the results of this approach using a Kinect sensor.

Performance comparison of leg detection methods using laser range finder

Human interaction becomes an important issue in the field of mobile robotics.To achieve human-friendly navigation,the robot needs to recognize human on cluttered backgrounds,and this can be fulfilled by the detection of human legs.The detection of human legs is advantageous because it enables detecting environmental obstacles at such heights.In this paper,we compared the performance of an algorithm using a single laser range finder(LRF)proposed in Ref.[1] with that of well-known feature extraction approaches-bounding box and circle fitting proposed in Ref.[2] by using the same laser scanned image.