CAIS: Culvert Autonomous Inspection Robotic System
Chuong Phuoc Le, Pratik Walunj, An Duy Nguyen, Yongyi Zhou, Binh Nguyen, Thang Nguyen, Anton Netchaev, Hung Manh La
Abstract
Culverts, essential components of drainage systems, require regular inspection to ensure optimal functionality. However, culvert inspections pose numerous challenges, including accessibility, manpower, defect localization, and reliance on superficial assessments. To address these challenges, we propose a novel Culvert Autonomous Inspection Robotic System (CAIS) equipped with advanced sensing and evaluation capabilities. Our solution integrates an RGBD camera, deep learning, lighting systems, and non-destructive evaluation (NDE) techniques to enable accurate and comprehensive condition assessments. We present a pioneering Partially Observable Markov Decision Process (POMDP) framework to resolve uncertainty in autonomous inspections, especially in confined and unstructured environments like culverts or tunnels. The framework outputs detailed 3D maps highlighting visual defects and NDE condition assessments, demonstrating consistent and reliable performance in both indoor and outdoor scenarios. Additionally, we provide an open-source implementation of our framework on GitHub, contributing to the advancement of autonomous inspection technology and fostering collaboration within the research community. Source codes are available *.
BibTeX
@inproceedings{iros2024_caisculvertauton,
title = {CAIS: Culvert Autonomous Inspection Robotic System},
author = {Chuong Phuoc Le and Pratik Walunj and An Duy Nguyen and Yongyi Zhou and Binh Nguyen and Thang Nguyen and Anton Netchaev and Hung Manh La},
booktitle = {IROS 2024},
year = {2024}
}