Improving odometry using a controlled point laser

Authors:
Tom Epton;Adam Hoover
Affiliations:
Electrical and Computer Engineering Department, Clemson University, Clemson, USA 29634-0915;Electrical and Computer Engineering Department, Clemson University, Clemson, USA 29634-0915
Venue:
Autonomous Robots
Year:
2012

Citing 9
Cited 0

Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

Journal of Intelligent and Robotic Systems
Odometry based pose determination and errors measurement for a mobile robot with two steerable drive wheels

Journal of Intelligent and Robotic Systems
FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics (Springer Tracts in Advanced Robotics)

FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics (Springer Tracts in Advanced Robotics)
Simultaneous localization and odometry self calibration for mobile robot

Autonomous Robots
A comparison of line extraction algorithms using 2D range data for indoor mobile robotics

Autonomous Robots
A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot

Autonomous Robots
On measuring the accuracy of SLAM algorithms

Autonomous Robots
A collection of outdoor robotic datasets with centimeter-accuracy ground truth

Autonomous Robots
A comparison of loop closing techniques in monocular SLAM

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

We describe methods to improve an odometry estimate of motion using a single point range-finding laser. The laser measures the distance to an arbitrary point on a flat surface. After some motion, the odometry estimate of the motion is used to aim the laser to measure the distance to the same point. Through geometric methods the discrepancy in the expected and measured distances can be used to update the odometer estimate of the motion. We present experiments using a mobile robot, pan-tilt unit and laser. Our method could be incorporated into any simultaneous localization and mapping (SLAM) algorithm.