Title
Combining Obstacle Avoidance and Visual Simultaneous Localization and Mapping for Indoor Navigation.
Abstract
People with disabilities (PWD) face a number of challenges such as obstacle avoidance or taking a minimum path to reach a destination while travelling or taking public transport, especially in airports or bus stations. In some cases, PWD, and specifically visually impaired people, have to wait longer to overcome these situations. In order to solve these problems, the computer-vision community has applied a number of techniques that are nonetheless insufficient to handle these situations. In this paper, we propose a visual simultaneous localization and mapping for moving-person tracking (VSLAMMPT) method that can assist PWD in smooth movement by knowing a position in an unknown environment. We applied expected error reduction with active-semisupervised-learning (EER-ASSL)-based person detection to eliminate noisy samples in dynamic environments. After that, we applied VSLAMMPT for effective smoothing, obstacle avoidance, and uniform navigation in an indoor environment. We analyze the joint approach symmetrically and applied the proposed method to benchmark datasets and obtained impressive performance.
Year
DOI
Venue
2020
10.3390/sym12010119
SYMMETRY-BASEL
Keywords
Field
DocType
SLAM,obstacle avoidance,depth estimation,object detection
Obstacle avoidance,Computer vision,Object detection,Mathematical analysis,Public transport,Person detection,Smoothing,Artificial intelligence,Simultaneous localization and mapping,Mathematics
Journal
Volume
Issue
Citations 
12
1
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
SongGuo Jin100.34
Minhaz Uddin Ahmed273.40
Jin Woo Kim300.34
Kim Yeong Hyeon400.34
Phill Kyu Rhee56024.82