人工智能
计算机视觉
计算机科学
同时定位和映射
Orb(光学)
特征(语言学)
管道(软件)
机器人
分割
移动机器人
图像(数学)
语言学
哲学
程序设计语言
作者
Shuhuan Wen,X.S. Li,Xin Liu,J.C. Li,Sheng Tao,Yidan Long,Tony Z. Qiu
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-11
被引量:1
标识
DOI:10.1109/tim.2023.3317378
摘要
Simultaneous localization and mapping (SLAM) has been widely used in augmented reality (AR), virtual reality (VR), robotics, and autonomous vehicles as the theoretical basis for robots to perceive their environment. Most popular SLAM algorithms assume that objects in the scene are static. Solving dynamic problems in SLAM is now attracting increasing attention. In this paper, we propose a method that combines semantic segmentation information and spatial motion information of associated pixels to cope with dynamic objects based on ORB-SLAM2. We add a deep segmentation network SegNet to segment input image and obtain the semantic information for each feature point. Next, the spatial velocity of feature points between adjacent frames is calculated assuming uniform motion. Finally, the two parts are fused for the final judgment, and the dynamic feature points are removed to improve positioning accuracy. We evaluate our SLAM algorithms using the public KITTI dataset. The proposed algorithm has a similar overall accuracy level to ORB-SLAM2, but it is more accurate in sequences with many dynamic objects. On KITTI’s raw data sequence containing multiple dynamic objects, our pipeline achieves the best performance, improving 39.5% compared with the original ORB-SLAM2 system. We compare our algorithm with other state-of-the-art SLAM systems used to cope with dynamic environments. The results show that the proposed algorithm has better performance.
科研通智能强力驱动
Strongly Powered by AbleSci AI