Wang Yi, Bu Haoyu, Zhang Xiaolong, Cheng Jia
College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China.
Sensors (Basel). 2022 Nov 7;22(21):8561. doi: 10.3390/s22218561.
Aiming at the problem that Simultaneous localization and mapping (SLAM) is greatly disturbed by many dynamic elements in the actual environment, this paper proposes a real-time Visual SLAM (VSLAM) algorithm to deal with a dynamic indoor environment. Firstly, a lightweight YoloFastestV2 deep learning model combined with NCNN and Mobile Neural Network (MNN) inference frameworks is used to obtain preliminary semantic information of images. The dynamic feature points are removed according to epipolar constraint and dynamic properties of objects between consecutive frames. Since reducing the number of feature points after rejection affects the pose estimation, this paper innovatively combines Cylinder and Plane Extraction (CAPE) planar detection. We generate planes from depth maps and then introduce planar and in-plane point constraints into the nonlinear optimization of SLAM. Finally, the algorithm is tested on the publicly available TUM (RGB-D) dataset, and the average improvement in localization accuracy over ORB-SLAM2, DS-SLAM, and RDMO-SLAM is about 91.95%, 27.21%, and 30.30% under dynamic sequences, respectively. The single-frame tracking time of the whole system is only 42.68 ms, which is 44.1%, being 14.6-34.33% higher than DS-SLAM, RDMO-SLAM, and RDS-SLAM respectively. The system that we proposed significantly increases processing speed, performs better in real-time, and is easily deployed on various platforms.
针对同步定位与地图构建（SLAM）在实际环境中受到诸多动态元素严重干扰的问题，本文提出一种实时视觉SLAM（VSLAM）算法来处理动态室内环境。首先，使用结合了NCNN和移动神经网络（MNN）推理框架的轻量级YoloFastestV2深度学习模型来获取图像的初步语义信息。根据极线约束和连续帧之间物体的动态特性去除动态特征点。由于剔除特征点后数量减少会影响位姿估计，本文创新性地结合了圆柱与平面提取（CAPE）平面检测。我们从深度图生成平面，然后将平面和平面内点约束引入到SLAM的非线性优化中。最后，该算法在公开可用的TUM（RGB-D）数据集上进行测试，在动态序列下，相对于ORB-SLAM2、DS-SLAM和RDMO-SLAM，定位精度的平均提升分别约为91.95%、27.21%和30.30%。整个系统的单帧跟踪时间仅为42.68毫秒，比DS-SLAM、RDMO-SLAM和RDS-SLAM分别高出44.1%、14.6 - 34.33%。我们提出的系统显著提高了处理速度，实时性能更好，并且易于在各种平台上部署。
