Open AccessHuman Following for Outdoor Mobile Robots Based on Point‐Cloud's Appearance Model
Author(s) -
Linxi GONG,
Yunfei CAI
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.07.017
Subject(s) - computer science , artificial intelligence , point cloud , computer vision , particle filter , adaboost , mobile robot , histogram , robot , exploit , classifier (uml) , filter (signal processing) , image (mathematics) , computer security
In this paper, we propose a point‐cloud‐based algorithm for human‐following robots to detect and follow the target person in a complex outdoor environment. Specifically, we exploit AdaBoost to train a binary classifier in a designed feature space based on sparse point‐cloud to distinguish the target person from other objects. Then a particle filter is applied to continuously track the target's position. Motivated by the interference of obstacles in long‐distance human‐following scenarios, a motion plan algorithm based on vector field histogram is adopted. Experiments are carried out both on the dataset we collected and in real application scenarios. The results show that our algorithm has the ability of real‐time target detection and tracking, and is robust to deal with complex situations in outdoor environments.