森を見て木を見る：CPU上で動作する都市・自然環境での深層レーザー自己位置推定
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
3D点群からトリプレット比較で特徴表現を学習し、CPUのみで動く小型モデルによるループ閉じ込み検出を実現した研究。
著者: Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.
