用全景激光雷达的深度与反射图像实现户外场景分类
Learning Geometric and Photometric Features from Panoramic LiDAR Scans for Outdoor Place Categorization
- 构建多模态全景3D户外数据集MPO，包含六类场景
- 融合深度与反射率信息，分类准确率超越传统方法
- 适合自动驾驶与机器人环境理解任务
语义场景分类是自主机器人和车辆在陌生环境中实现自主决策与导航的关键任务。由于光照变化（如24小时周期）和车辆、行人遮挡等感知差异，户外场景比室内更难处理。本文提出一种基于卷积神经网络（CNN）的新方法，输入为3D激光雷达生成的全向深度/反射率图像。首先，构建大规模户外场景数据集MPO，包含两种不同激光雷达采集的点云数据，标注六类户外场景：海岸、森林、室内外停车场、住宅区、城市区域。其次，设计适用于激光雷达的分类CNN模型，并在MPO数据集上评估。实验结果表明，同时使用深度与反射率模态的方法优于传统方法，验证了多模态特征的有效性。通过可视化训练后的深层网络特征，进一步分析其学习机制。
原文摘要 · Abstract (English)
Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamiliar environments. In particular, outdoor places are more difficult targets than indoor ones due to perceptual variations, such as dynamic illuminance over twenty-four hours and occlusions by cars and pedestrians. This paper presents a novel method of categorizing outdoor places using convolutional neural networks (CNNs), which take omnidirectional depth/reflectance images obtained by 3D LiDARs as the inputs. First, we construct a large-scale outdoor place dataset named Multi-modal Panoramic 3D Outdoor (MPO) comprising two types of point clouds captured by two different LiDARs. They are labeled with six outdoor place categories: coast, forest, indoor/outdoor parking, residential area, and urban area. Second, we provide CNNs for LiDAR-based outdoor place categorization and evaluate our approach with the MPO dataset. Our results on the MPO dataset outperform traditional approaches and show the effectiveness in which we use both depth and reflectance modalities. To analyze our trained deep networks we visualize the learned features.
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