InstaLoc: インスタンス学習による屋内環境でのワンショット全球LiDAR自己位置推定
InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning
屋内環境において、単一のLiDARスキャンから事前地図内での自己位置を推定する手法。パノプティックセグメンテーションとインスタンス記述子を用いて物体を認識・照合し、6自由度の姿勢を推定する。
著者: Lintong Zhang, Tejaswi Digumarti, Georgi Tinchev, Maurice Fallon
分類: cs.RO
原文アブストラクト
Localization for autonomous robots in prior maps is crucial for their functionality. This paper offers a solution to this problem for indoor environments called InstaLoc, which operates on an individual lidar scan to localize it within a prior map. We draw on inspiration from how humans navigate and position themselves by recognizing the layout of distinctive objects and structures. Mimicking the human approach, InstaLoc identifies and matches object instances in the scene with those from a prior map. As far as we know, this is the first method to use panoptic segmentation directly inferring on 3D lidar scans for indoor localization. InstaLoc operates through two networks based on spatially sparse tensors to directly infer dense 3D lidar point clouds. The first network is a panoptic segmentation network that produces object instances and their semantic classes. The second smaller network produces a descriptor for each object instance. A consensus based matching algorithm then matches the instances to the prior map and estimates a six degrees of freedom (DoF) pose for the input cloud in the prior map. The significance of InstaLoc is that it has two efficient networks. It requires only one to two hours of training on a mobile GPU and runs in real-time at 1 Hz. Our method achieves between two and four times more detections when localizing, as compared to baseline methods, and achieves higher precision on these detections.
