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Sliding Window Mapping for Omnidirectional RGB-D Sensors.

Nicolas Dalmedico¹, Marco Antônio Simões Teixeira¹, Higor Barbosa Santos¹

¹Graduate Program in Electrical and Computer Engineering (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba PR 80230-901, Brazil.

Sensors (Basel, Switzerland)

|November 27, 2019

Summary

This summary is machine-generated.

This study introduces an omnidirectional RGB-D sensor prototype with a novel mapping strategy for robots. It enables 360-degree environment perception and reconstruction for tasks like human detection.

Keywords:

LIDAR RGB-D sensor deep-learning mapping mobile robots omnidirectional

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Area of Science:

Robotics
Computer Vision
Sensor Technology

Background:

Robots require comprehensive environmental perception for navigation and interaction.
Existing sensors often have limited fields of view, necessitating complex configurations for omnidirectional sensing.

Purpose of the Study:

To develop an omnidirectional RGB-D sensor prototype.
To create a novel mapping strategy tailored to the sensor's capabilities.
To enable robust environment reconstruction and human detection for mobile robots.

Main Methods:

An actuated Light Detection and Ranging (LIDAR) sensor combined with an RGB camera.
A 90-degree tilting mechanism and rotational capability for the LIDAR sensor to achieve omnidirectional scanning.
A dual-map strategy: a local 2D map for immediate perception and a global map for memory.

Main Results:

The prototype successfully gathers RGB and 3D data from all directions.
The proposed mapping strategy effectively utilizes sensor data for environment reconstruction.
The system demonstrates potential for accurate human detection within the robot's environment.

Conclusions:

The developed omnidirectional RGB-D sensor and mapping strategy enhance robot perception capabilities.
This approach offers a flexible and effective solution for 3D environment mapping and analysis.
The system provides a foundation for advanced robotic applications requiring full-scene awareness.