NRSL实验室硕士生梁潇所发表的学术论文“Visual Laser-SLAM in Large-scale Indoor Environments”荣获ROBIO2016的T.J. Tarn Best Paper in Robotics Finalist。
ROBIO，全称IEEE International Conference on Robotics and Biomimetics，机器人与仿生技术国际学会，IEEE ROBIO是继ICRA和IROS之后，国际机器人学界的又一个旗舰会议，每年举办1次。
The published paper “Visual Laser-SLAM in Large-scale Indoor Environments” by a master from NRSL won the T.J. Tarn Best Paper in Robotics Finalist of Robio2016.
Here is the abstract of the paper:
Loop closure is a well-known problem in the research of laser based simultaneous localization and mapping, especially for applications in large-scale environments. The cumulative errors in the estimated pose and map make the loop detection difficult, no matter using particle filter-based or graph-based SLAM methods. Camera has the advantage of rich information but suffers from short distance and relative high computation burden. In this paper, we proposed a novel approach to address the loop closure problem in large-scale laser-SLAMs, where both laser and camera sensors are integrated. ORB features and bags-of-word were applied to obtain fast and robust performance of loop detection. The well-recognized LRGC SLAM framework and SPA optimization algorithm were then used to achieve the SLAM. Finally, several experiments in different large-scale environments were performed to verify the effectiveness of the proposed approach.