同时定位和映射
人工智能
计算机科学
计算机视觉
最小边界框
跳跃式监视
稳健性(进化)
移动机器人
机器人
图像(数学)
生物化学
化学
基因
作者
Can Gong,Ying Sun,Chunlong Zou,Bo Tao,Li Huang,Zifan Fang,Dalai Tang
标识
DOI:10.1088/1361-6501/ad2669
摘要
Abstract Within the realm of autonomous robotic navigation, simultaneous localization and mapping (SLAM) serves as a critical perception technology, drawing heightened attention in contemporary research. The traditional SLAM systems perform well in static environments, but in the real physical world, dynamic objects can destroy the static geometric constraints of the SLAM system, further limiting its practical application in the real world. In this paper, a robust dynamic RGB-D SLAM system is proposed to expand the number of static points in the scene by combining with YOLO-Fastest to ensure the effectiveness of the geometric constraints model construction, and then based on that, a new thresholding model is designed to differentiate the dynamic features in the objection bounding box, which takes advantage of the double polyline constraints and the residuals after reprojection to filter the dynamic feature points. In addition, two Gaussian models are constructed to segment the moving objects in the bounding box in the depth image to achieve the effect similar to the instance segmentation under the premise of ensuring the computational speed. In this paper, experiments are conducted on dynamic sequences provided by the TUM dataset to evaluate the performance of the proposed method, and the results show that the root mean squared error metric of the absolute trajectory error of the algorithm of this paper has at least 80% improvement compared to ORB-SLAM2. Higher robustness in dynamic environments with both high and low dynamic sequences compared to DS-SLAM and Dynaslam, and can effectively provide intelligent localization and navigation for mobile robots.
科研通智能强力驱动
Strongly Powered by AbleSci AI