Integration of People Detection and Simultaneous Localization and Mapping Systems for an Autonomous Robotic Platform

Abstract

This paper presents the implementation of a people detection system for a robotic platform able to perform Simultaneous Localization and Mapping (SLAM), allowing the exploration and navigation of the robot considering people detection interaction. The robotic platform consists of a Pioneer 3DX robot equipped with an RGB-D camera, a Sick Lms200 sensor laser and a computer using the robot operating system ROS. The idea is to integrate the people detection system to the simultaneous localization and mapping (SLAM) system of the robot using ROS. Furthermore, this paper presents an evaluation of two different approaches for the people detection system. The first one uses a manual feature extraction technique, and the other one is based on deep learning methods. The manual feature extraction method in the first approach is based on HOG (Histogram of Oriented Gradients) detectors. The accuracy of the techniques was evaluated using two different libraries. The PCL library (Point Cloud Library) implemented in C ++ and the VLFeat MatLab library with two HOG variants, the original one, and the DPM (Deformable Part Model) variant. The second approaches are based on a Deep Convolutional Neural Network (CNN), and it was implemented using the MatLab MatConvNet library. Tests were made objecting the evaluation of losses and false positives in the people's detection process in both approaches. It allowed us to evaluate the people detection system during the navigation and exploration of the robot, considering the real time interaction of people recognition in a semi-structured environment.