Sarath Kodagoda — accepted AI-conference papers

2025

Relative Velocity-Based Reward Model for Socially-Aware Navigation with Deep Reinforcement Learning

Vinu Maddumage, Sarath Kodagoda, Marc G. Carmichael, Amal Gunatilake, Karthick Thiyagarajan, Jodi Martin

ICRA 2025

Mobile robots are increasingly deployed in shared environments where they must learn to navigate alongside humans. Deep Reinforcement Learning (DRL) techniques have shown promise in developing navigation policies that account for interactions within crowds, fostering socially acceptable movement. Ho

Cited by 1 Source ↗Scholar ↗

2022

A Novel UHF-RFID Dual Antenna Signals Combined With Gaussian Process and Particle Filter for In-Pipe Robot Localization

Amal Gunatilake, Sarath Kodagoda, Karthick Thiyagarajan

RA-L 2022

Condition assessment of underground infrastructures such as pipe networks is crucial for aging cities around the globe. Recent development of robotic technologies facilitated application of them in condition assessment of pipe networks. However, there is still a gap for accurate localization technol

Cited by 16 Source ↗Scholar ↗

2017

Parse geometry from a line: Monocular depth estimation with partial laser observation

Yiyi Liao, Lichao Huang, Yue Wang, Sarath Kodagoda, Yinan Yu, Yong Liu

ICRA 2017poster

Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing capability, knowledge of depth is an essential part in many robotics activities. Therefore, recently, there is an increas…

Cited by 132 Source ↗Scholar ↗

2016

Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles

Dinuka M. W. Abeywardena, Shoudong Huang, Ben Barnes, Gamini Dissanayake, Sarath Kodagoda

ICRA 2016

The main contribution of this paper is a high frequency, low-complexity, on-board visual-inertial odometry system for quadrotor micro air vehicles. The system consists of an extended Kalman filter (EKF) based state estimation algorithm that fuses information from a low cost MEMS inertial measurement

Cited by 14 Source ↗Scholar ↗

2016

Understand scene categories by objects: A semantic regularized scene classifier using Convolutional Neural Networks

Yiyi Liao, Sarath Kodagoda, Yue Wang, Lei Shi, Yong Liu

ICRA 2016

Scene classification is a fundamental perception task for environmental understanding in today's robotics. In this paper, we have attempted to exploit the use of popular machine learning technique of deep learning to enhance scene understanding, particularly in robotics applications. As scene images

Cited by 109 Source ↗Scholar ↗