Autonomous Navigation by Robust Scan Matching Technique

TL;DR

This paper presents a robust laser scan matching technique for autonomous robot navigation that effectively estimates the robot's pose even with noisy sensor data by combining image processing, outlier rejection, and Kalman filtering.

Contribution

A novel scan matching method using Harris corner detection and RANSAC for outlier rejection, integrated with Extended Kalman Filter for improved pose estimation in noisy environments.

Findings

The proposed method outperforms conventional ICP in noisy conditions.

It achieves accurate navigation with minimal additional computational cost.

The technique is validated on a laboratory robot setup.

Abstract

For effective autonomous navigation,estimation of the pose of the robot is essential at every sampling time. For computing an accurate estimation,odometric error needs to be reduced with the help of data from external sensor. In this work, a technique has been developed for accurate pose estimation of mobile robot by using Laser Range data. The technique is robust to noisy data, which may contain considerable amount of outliers. A grey image is formed from laser range data and the key points from this image are extracted by Harris corner detector. The matching of the key points from consecutive data sets have been done while outliers have been rejected by RANSAC method. Robot state is measured by the correspondence between the two sets of keypoints. Finally, optimal robot state is estimated by Extended Kalman Filter. The technique has been applied to an operational robot in the laboratory environment to show the robustness of the technique in presence of noisy sensor data. The performance of this new technique has been compared with that of conventional ICP method. Through this method, effective and accurate navigation has been achieved even in presence of substantial noise in the sensor data at the cost of a small amount of additional computational complexity.