基本信息
浏览量:163

个人简介
My research i in the area of mobile robotics. The have comprised be underwater, flying, outdoor ground robots, autonomous driving and indoor robots. Currently the focus in our group is underwater robots. The main challenge remains not getting lost, (so called simultaneous localization and mapping SLAM). Place recognition underwater is very different than in man made environments. Most of the features one observes are dynamic on medium time scales, ice melts, sea creatures crawl and so on. Place recognition is intrinsically ambiguous and calls for careful inference. For autonomous driving I am interested in situation awareness for decision support. Outdoor robots and indoor robots this has moved into higher levels or spatial representation, semantic mapping, spatial-temporal models.
Underwater SLAM has so far has consisted in relatively few trials of tried and true methods from outdoor land robots to autonomous underwater vehicles, AUV. This works when only to the extend that the sensors and the environment can be treated as similar to the land analog. Natural underwater environments are however for the most part quite different. The features available as navigation aids are not always possible to characterize by the methods common to describing keypoints in images. Sonar has a much different type of viewpoint dependance as compared to cameras and lidar. There will always be long transits where the bottom is not in sensor range or where the sensors return 'blank' featureless measurements. Motion estimation without acoustic contact with the seabed is inherently very uncertain. New approaches for underwater SLAM will have an impact here.
At the same time AUVs to be effective will need to travel beyond limits of underwater communications. This makes the future AUV scenarios the most autonomous of all robot scenarios. These robot must operate without human instruction in areas and situations that are unknowable at the time of launching the AUV. Missions under ice or to the deep ocean are examples. These envisioned missions will take days to months to carry out with no communications. Can these robots navigate using the sonar and other clues. The data that they collect is of little value if it is not geo-referenced.
Underwater SLAM has so far has consisted in relatively few trials of tried and true methods from outdoor land robots to autonomous underwater vehicles, AUV. This works when only to the extend that the sensors and the environment can be treated as similar to the land analog. Natural underwater environments are however for the most part quite different. The features available as navigation aids are not always possible to characterize by the methods common to describing keypoints in images. Sonar has a much different type of viewpoint dependance as compared to cameras and lidar. There will always be long transits where the bottom is not in sensor range or where the sensors return 'blank' featureless measurements. Motion estimation without acoustic contact with the seabed is inherently very uncertain. New approaches for underwater SLAM will have an impact here.
At the same time AUVs to be effective will need to travel beyond limits of underwater communications. This makes the future AUV scenarios the most autonomous of all robot scenarios. These robot must operate without human instruction in areas and situations that are unknowable at the time of launching the AUV. Missions under ice or to the deep ocean are examples. These envisioned missions will take days to months to carry out with no communications. Can these robots navigate using the sonar and other clues. The data that they collect is of little value if it is not geo-referenced.
研究兴趣
论文共 131 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
IEEE JOURNAL OF OCEANIC ENGINEERING (2025)
arxiv(2025)
引用0浏览0引用
0
0
CoRR (2024)
引用0浏览0EI引用
0
0
IEEE Robotics and Automation Letterspp.1-8, (2024)
CoRR (2024)
IEEE JOURNAL OF OCEANIC ENGINEERING (2024)
2024 35TH IEEE INTELLIGENT VEHICLES SYMPOSIUM, IEEE IV 2024pp.2405-2412, (2024)
2024 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WORKSHOPS, WACVW 2024pp.988-994, (2024)
加载更多
作者统计
#Papers: 131
#Citation: 3357
H-Index: 30
G-Index: 55
Sociability: 5
Diversity: 2
Activity: 18
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn