LiDARをカメラセンサとして用いた汎用深層学習の検出・セグメンテーションモデルの分析
Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor
LiDARが出力する深度・反射率・近赤外の低解像度360度画像を、視覚カメラ向けの汎用検出・セグメンテーション深層学習モデルで処理できることを示し、性能を定性的・定量的に評価した。
著者: Yu Xianjia, Sahar Salimpour, Jorge Peña Queralta, Tomi Westerlund
分類: cs.RO, cs.CV
原文アブストラクト
Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explores the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with 360\textdegree field of view obtained with lidar sensors by encoding either depth, reflectivity, or near-infrared light in the image pixels. We show that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions where vision sensors present inherent limitations. We provide both a qualitative and quantitative analysis of the performance of a variety of neural network architectures. We believe that using DL models built for visual cameras offers significant advantages due to the much wider availability and maturity compared to point cloud-based perception.
