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Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar.

Hasan Ismail¹, Rohit Roy¹, Long-Jye Sheu²

¹Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

Sensors (Basel, Switzerland)

|February 26, 2022

Summary

This summary is machine-generated.

This study presents an exploration-based simultaneous localization and mapping (e-SLAM) method for indoor mobile robots (IMRs) using LiDAR. This technique enables robots to map unknown environments without pre-existing data, reducing setup time for service robots.

Keywords:

LiDAR SLAM active localization exploration

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Indoor mobile robots (IMRs) require simultaneous localization and mapping (SLAM) to navigate and build maps of unexplored environments.
Existing SLAM methods often necessitate pre-scanned maps, increasing installation time for service robots.

Purpose of the Study:

To develop and implement an exploration-based SLAM (e-SLAM) method for IMRs using only LiDAR data.
To enable IMRs to construct maps of unknown indoor spaces, such as office buildings, without prior mapping.

Main Methods:

Deriving LiDAR mesh from orthogonal walls and filtering static/dynamic obstacles.
Defining LiDAR pose frame using coordinate transformation and navigation prediction.
Calculating IMR displacement from most significant corners (MSC) and orientation using linear quadratic estimation (LQE).

Main Results:

Real-time mapping and localization achieved on a single processor.
Demonstrated successful mapping in a complex engineering school building with labs, corridors, and a lobby.
Verified consistency of LiDAR-based navigation with IMU (Inertial Measurement Unit) data.

Conclusions:

The e-SLAM technique offers a viable solution for in-house service robots to operate in unexplored areas without pre-scan maps.
This method significantly reduces the initial installation and setup time for service robots.
LiDAR-based SLAM is a reliable and efficient approach for indoor robot navigation.