Andrzej Reinke — accepted AI-conference papers

2026

World Model Failure Classification and Anomaly Detection for Autonomous Inspection

Michelle Ho, Muhammad Fadhil Ginting, Isaac Ronald Ward, Andrzej Reinke, Mykel J. Kochenderfer, Ali-akbar Agha-mohammadi +1

ICRA 2026poster

Autonomous inspection robots for monitoring industrial sites can reduce costs and risks associated with human-led inspection. However, accurate readings can be challenging due to occlusions, limited viewpoints, or unexpected environmental conditions. We propose a hybrid framework that combines super…

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2022

LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments

Yun Chang, Kamak Ebadi, Christopher E. Denniston, Muhammad Fadhil Ginting, Antoni Rosinol, Andrzej Reinke +6

RA-L 2022

Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale underground environments requires high-precision localization and mapping. This crucial requirement is faced with many challenges in complex and perceptually-degraded subterranean environments, as the onboard per

Cited by 154 Source ↗Scholar ↗

2022

LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping

Andrzej Reinke, Matteo Palieri, Benjamin Morrell, Yun Chang, Kamak Ebadi, Luca Carlone +1

RA-L 2022

Lidar odometry has attracted considerable attention as a robust localization method for autonomous robots operating in complex GNSS-denied environments. However, achieving reliable and efficient performance on heterogeneous platforms in large-scale environments remains an open challenge due to the l

Cited by 86 Source ↗Scholar ↗

2022

Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM

Christopher E. Denniston, Yun Chang, Andrzej Reinke, Kamak Ebadi, Gaurav S. Sukhatme, Luca Carlone +2

RA-L 2022

Multi-robot SLAM systems in GPS-denied environments require loop closures to maintain a drift-free centralized map. With an increasing number of robots and size of the environment, checking and computing the transformation for all the loop closure candidates becomes computationally infeasible. In th

Cited by 31 Source ↗code ↗Scholar ↗

2021

Simple But Effective Redundant Odometry for Autonomous Vehicles

Andrzej Reinke, Xieyuanli Chen, Cyrill Stachniss

ICRA 2021poster

Robust and reliable ego-motion is a key component of most autonomous mobile systems. Many odometry estimation methods have been developed using different sensors such as cameras or LiDARs. In this work, we present a resilient approach that exploits the redundancy of multiple odometry algorithms usin…

Cited by 15 Source ↗code ↗Scholar ↗