Demo Data from EDBT 2026 / reviewers in the wild / expert
Warren Whittaker
dblp:42/1437
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Robot navigation and mapping · 95% Reinforcement learning · 3% Planning, search and constraint satisfaction · 2% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › SLAM
multi-sensor SLAM |
0.8 | 1 | 2024 | SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments · CVPR 2024 |
Robotics › Robot navigation and mapping › SLAM
robust SLAM |
0.8 | 1 | 2024 | SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments · CVPR 2024 |
Robotics › Robot navigation and mapping
SLAM |
0.8 | 1 | 2024 | SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments · CVPR 2024 |
Machine learning › Reinforcement learning › exploration
autonomous exploration |
0.0 | 1 | 2004 | A Campaign in Autonomous Mine Mapping · ICRA 2004 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › plan execution
failure recovery |
0.0 | 1 | 2004 | A Campaign in Autonomous Mine Mapping · ICRA 2004 |
Machine learning › Reinforcement learning › exploration › autonomous exploration › mobile robot exploration
unknown environment exploration |
0.0 | 1 | 2004 | A Campaign in Autonomous Mine Mapping · ICRA 2004 |
Methods — techniques the papers use, named apart from their topics
LiDAR-inertial-thermal fusion · 0.8mapping algorithms · 0.0autonomous exploration · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | SubT-MRS Dataset: Pushing SLAM Towards All-weather EnvironmentsabstractSimultaneous localization and mapping (SLAM) is a fundamental task for numerous applications such as autonomous navigation and exploration. Despite many SLAM datasets have been released, current SLAM solutions still struggle to have sustained and resilient performance. One major issue is the absence of high-quality datasets including diverse all-weather conditions and a reliable metric for assessing robustness. This limitation significantly restricts the scalability and generalizability of SLAM technologies, impacting their development, validation, and deployment. To address this problem, we present SubT-MRS, an ex-tremely challenging real-world dataset designed to push SLAM towards all-weather environments to pursue the most robust SLAM performance. It contains multi-degraded en-vironments including over 30 diverse scenes such as structureless corridors, varying lighting conditions, and perceptual obscurants like smoke and dust; multimodal sensors such as LiDAR, fisheye camera, IMU, and thermal camera; and multiple locomotions like aerial, legged, and wheeled robots. We developed accuracy and robustness evaluation tracks for SLAM and introduced novel robustness metrics. Comprehensive studies are performed, revealing new obser-vations, challenges, and opportunities for future research. Shibo Zhao, Yuanjun Gao, Damanpreet Singh, Rushan Jiang, Haoxiang Sun, Mansi Sarawata, Yuheng Qiu, Warren Whittaker, Ian Higgins, Yi Du 0001, Shaoshu Su, John Keller, Jay Karhade, Lucas Nogueira, Sourojit Saha, Ji Zhang 0003, Chen Wang 0033, Sebastian A. Scherer |
CVPR | 9 |
| 2004 | A Campaign in Autonomous Mine MappingabstractUnknown, unexplored and abandoned subterranean voids threaten mining operations, surface developments and the environment. Hazards within these spaces preclude human access to create and verify extensive maps or to characterize and analyze the environment. To that end, we have developed a mobile robot capable of autonomously exploring and mapping abandoned mines. To operate without communications in a harsh environment with little chance of rescue, this robot must have a robust electro-mechanical platform, a reliable software system, and a dependable means of failure recovery. Presented are the mechanisms, algorithms, and analysis tools that enable autonomous mine exploration and mapping along with extensive experimental results from eight successful deployments into the abandoned Mathies coal mine near Pittsburgh, PA. Christopher R. Baker, Aaron Morris, David I. Ferguson, Scott Thayer, Warren Whittaker, Zachary Omohundro, Carlos F. Reverte, Dirk Hähnel, Sebastian Thrun |
ICRA | 5 |