论文标题
Prob-Slam：基于概率图优化的实时视觉大满贯
PROB-SLAM: Real-time Visual SLAM Based on Probabilistic Graph Optimization
论文作者
论文摘要
传统的大满贯算法通常基于缺乏高级信息的人工特征。通过引入语义信息，SLAM可以拥有更高的稳定性和鲁棒性，而不是纯粹手工制作的功能。但是，语义检测网络的高不确定性禁止高级信息的实际功能。为了解决语义引入的不确定性属性，本文提出了基于高斯分布假设的新概率图。该地图将语义二进制对象检测转换为概率结果，从而有助于建立人工特征和语义信息之间的概率数据关联。通过我们的算法，在每个更新步骤中将给予更高的置信度，而检测区域的边缘将以较低的信心赋予。然后，不确定性受到破坏，对非线性优化的影响较小。实验是在TUM RGBD数据集中进行的，结果表明，在室内环境的错误中，我们的系统将ORB-SLAM2提高了约15％。我们已经证明该方法可以成功应用于包含动态对象的环境。
Traditional SLAM algorithms are typically based on artificial features, which lack high-level information. By introducing semantic information, SLAM can own higher stability and robustness rather than purely hand-crafted features. However, the high uncertainty of semantic detection networks prohibits the practical functionality of high-level information. To solve the uncertainty property introduced by semantics, this paper proposed a novel probability map based on the Gaussian distribution assumption. This map transforms the semantic binary object detection into probability results, which help establish a probabilistic data association between artificial features and semantic info. Through our algorithm, the higher confidence will be given higher weights in each update step while the edge of the detection area will be endowed with lower confidence. Then the uncertainty is undermined and has less effect on nonlinear optimization. The experiments are carried out in the TUM RGBD dataset, results show that our system improves ORB-SLAM2 by about 15% in indoor environments' errors. We have demonstrated that the method can be successfully applied to environments containing dynamic objects.
