DepthLiDAR: Active Segmentation of Environment Depth Map Into Mobile Sensors

Abstract

This paper presents a novel approach for creating virtual LiDAR scanners through the active segmentation of point clouds. The method employs top-view point cloud segmentation in virtual LiDAR sensors that can be applied to the intelligent behavior of autonomous agents. Segmentation is correlated with the visual tracking of the agent for localization in the environment and point cloud. Virtual LiDAR sensors with different characteristics and positions can then be generated. This method is referred to as the DepthLiDAR approach, and is rigorously evaluated to quantify its performance and determine its advantages and limitations. An extensive set of experiments is conducted using real and virtual LiDAR sensors to compare both approaches. The objective is to propose a novel method to incorporate spatial perception in warehouses, aiming to achieve Industry 4.0. Thus, it is tested in a low-scale warehouse to incorporate realistic features. The analysis of the experiments shows a measurement improvement of 52.24% compared to the conventional LiDAR.

Improving the Mobile Robots Indoor Localization System by Combining SLAM with Fiducial Markers

“…For example, fiducial markers are used as a complementary localization system for a Unmanned Aerial Vehicle (UAV) in power distribution line inspections [16], in simulations for target tracking for UAVs [17], and localize mobile robots in a smart warehouse system with fiducial markers positioned on top of the robots and the camera positioned on the warehouse ceiling [18]. Similarity, visual markers are captured by a ceiling-mounted camera to position virtual LiDAR sensors on small robots [19]. Fiducial markers are also applied as a method to improve the localization of a turtlebot robot in an indoor environment, combining the tracking position of the tags in walls with SLAM techniques and the Kalman filter algorithm, achieving an autonomous navigation system [20].…”

An Onboard Point Cloud Semantic Segmentation System for Robotic Platforms

“…For example, ground mobile robots can collect a 3D point cloud through LiDAR (light detection and ranging) devices; and underwater robots can use sonar devices to acquire a 2D point cloud of the sea environment. Hence, from both an academic and industrial perspective, point-cloud-based scene perception has attracted great attention [2,3].…”

Decisão Consensual em Sistemas Multirrobôs

“…Esse trabalho foi contribuic ¸ão direta para um congresso [Kalempa et al 2018], dois capítulos de livros [Kalempa et al 2020b] [Kalempa et al 2021b] e duas revistas internacionais [Kalempa et al 2020a] [Kalempa et al 2021a]. Contribuic ¸ões secundárias deste trabalho estão presentes em um congresso [Limeira et al 2019], um capítulo de livro [Limeira et al 2020] e duas revistas internacionais [Piardi et al 2019] [Limeira et al 2021].…”