Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
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DOI：
10.1109/lra.2020.2965390
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发表时间：
2019-09
影响因子：
5.2
通讯作者：
Lu Gan;Ray Zhang;J. Grizzle;R. Eustice;Maani Ghaffari
中科院分区：
文献类型：
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作者：
Lu Gan;Ray Zhang;J. Grizzle;R. Eustice;Maani Ghaffari
通过将贝叶斯核推理模型用于构建占用图的二元化问题推广到语义图的多类问题，提出了一种基于噪声点云的贝叶斯连续三维语义占用图。该方法为占用概率和语义概率提供了一个统一的概率模型，当所获得的度量中只存在一个占用类时，该方法很好地恢复到了原始的占用映射框架。贝叶斯空间核推理放松了独立网格的假设，使地图推理具有光滑性和连续性，能够利用环境中存在的局部相关性，提高了性能。随附的软件使用多线程和矢量化，在笔记本电脑CPU上以大约2$\mathm{hz}$的速度运行。使用多个立体摄像机和LiDAR数据集序列进行的评估表明，该方法的性能始终优于当前基线。我们还使用密歇根大学北校区两足机器人平台收集的数据进行了定性评估。
This article develops a Bayesian continuous 3D semantic occupancy map from noisy point clouds by generalizing the Bayesian kernel inference model for building occupancy maps, a binary problem, to semantic maps, a multi-class problem. The proposed method provides a unified probabilistic model for both occupancy and semantic probabilities and nicely reverts to the original occupancy mapping framework when only one occupied class exists in obtained measurements. The Bayesian spatial kernel inference relaxes the independent grid assumption and brings smoothness and continuity to the map inference, enabling to exploit local correlations present in the environment and increasing the performance. The accompanying software uses multi-threading and vectorization, and runs at about 2 $\mathrm{Hz}$ on a laptop CPU. Evaluations using multiple sequences of stereo camera and LiDAR datasets show that the proposed method consistently outperforms current baselines. We also present a qualitative evaluation using data collected with a bipedal robot platform on the University of Michigan - North Campus.