MLP-SLAM：基于多层感知机的同步定位与建图
MLP-SLAM: Multilayer Perceptron-Based Simultaneous Localization and Mapping
AI总结：
本文提出MLP-SLAM，利用多层感知机对双目视觉特征进行动态与静态分类并保留完整几何信息，同时发布超5万特征点数据集，在KITTI上取得最高平均精度和最快速度。
摘要：
视觉同步定位与建图（V-SLAM）系统近年来取得了显著发展，在动态物体有限的环境中展现出高精度。然而，当部署在可移动物体较多的场景中时，例如行人、汽车和公交车常见于户外场景的环境，其性能会显著下降。为解决这一问题，我们提出了一种基于多层感知机（MLP）的实时双目SLAM系统，该系统利用完整几何信息以避免信息丢失。此外，目前尚无公开可用的数据集可直接评估动态与静态特征分类方法的有效性，为弥补这一空白，我们创建了一个包含超过50,000个特征点的公开数据集。实验结果表明，在该数据集上，我们基于MLP的动态与静态特征点判别器相较于其他方法取得了更优的性能。此外，与其他动态SLAM系统相比，基于MLP的实时双目SLAM系统在户外KITTI跟踪数据集上展现出最高的平均精度和最快的速度。开源代码和数据集可在 https://github.com/TaozheLi/MLP-SLAM 获取。
英文摘要：
The Visual Simultaneous Localization and Mapping (V-SLAM) system has seen significant development in recent years, demonstrating high precision in environments with limited dynamic objects. However, their performance significantly deteriorates when deployed in settings with a higher presence of movable objects, such as environments with pedestrians, cars, and buses, which are common in outdoor scenes. To address this issue, we propose a Multilayer Perceptron (MLP)-based real-time stereo SLAM system that leverages complete geometry information to avoid information loss. Moreover, there is currently no publicly available dataset for directly evaluating the effectiveness of dynamic and static feature classification methods, and to bridge this gap, we have created a publicly available dataset containing over 50,000 feature points. Experimental results demonstrate that our MLP-based dynamic and static feature point discriminator has achieved superior performance compared to other methods on this dataset. Furthermore, the MLP-based real-time stereo SLAM system has shown the highest average precision and fastest speed on the outdoor KITTI tracking datasets compared to other dynamic SLAM systems.The open-source code and datasets are available at https://github.com/TaozheLi/MLP-SLAM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
