http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Jorn Thielecke (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Determining Potential Obstacles in Unobservable Areas Based on Current and Past Perception
Julia Baumgartner,Henrik Bey,Dennis Faßbender,Jorn Thielecke 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
Automated vehicles perceive only a small part of their environment. Especially unobservable vehicles pose a significant risk. To achieve safe but also comfortable behavior, potential, unobservable vehicles must be considered in behavior planning. Conventional methods use solely the current observation of the environment to determine potential obstacles. Past observations are rarely considered, although these may contain helpful information to rule out potential obstacle positions. This paper presents a novel algorithm that uses past observations besides the current observation to determine possible obstacle states. By means of a particle filter, we iteratively predict and filter feasible states of a potential obstacle. This results in a probability distribution for the position and velocity of an unobservable obstacle. We furthermore present a concept for the interface between our method and a basic behavior planning algorithm. The real-time capable method is tested on both simulated and real-world data. By comparing the algorithm to a baseline algorithm which uses only the current observation, we show that our algorithm prevents overly cautious assumptions about a potential obstacle’s state in certain situations. As a result, a more comfortable driving behavior can be achieved.
-
Incorporating Building Information to Globalize and Robustify Grid-Based Indoor SLAM
Markus Hiller,Florian Particke,Lucila Pati?no-Studencki,Jorn Thielecke 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
Navigation is one of the key topics in the field of mobile robotics. In many areas of application like Industry 4.0 or fully automated parking, additional external sensors are available as well as pre-existing knowledge about the general building structure. This information is of significant advantage, especially in situations where the perceptive field of the mobile platform is occluded. One method to gain the required information is the probabilistic concept of simultaneous localization and mapping (SLAM). However, most existing approaches generally lack the possibility to incorporate preexisting and external information. In this paper, a solution to the SLAM problem based on a Rao-Blackwellized particle filter is adopted to provide efficient means for exploiting such data. We present an approach that directly incorporates environment knowledge using a context-aware environment model, while establishing reference to a global coordinate frame allowing for a straight-forward fusion with external information sources. The evaluation is performed on real-world data obtained by a mobile platform. The qualitative analysis shows significant improvement in map quality and robustness regarding short sensor outages or reduced perception rates. Establishing a uniform reference frame and reusing data, the proposed approach clearly extends the functional range of SLAM, demonstrating substantial advantages over existing methods.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
