日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

自己位置推定arXiv:1910.10550

3次元LiDARから抽出したポールランドマークによる都市環境での長期車両自己位置推定

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

シェア:XThreadsFacebookLINEはてブBluesky

3次元LiDARからポール状物体を検出して地図を作成し、15か月にわたる都市環境での長期自己位置推定を高精度かつ高信頼で実現するシステムを提案した。

著者: Alexander Schaefer, Daniel Büscher, Johan Vertens, Lukas Luft, Wolfram Burgard

分類: cs.RO, cs.CV

原文アブストラクト

Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation at www.github.com/acschaefer/polex. In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the system with localizing a mobile robot over the course of 15~months in an urban area based on an initial map, confronting it with constantly varying routes, differing weather conditions, seasonal changes, and construction sites. Second, we show that the proposed approach clearly outperforms a recently published method in terms of accuracy.

関連論文

  1. 1Boston Dynamics、車輪付きAtlasの研究試作機を紹介研究・技術
  2. 2GXOとApptronik、Apolloの物流現場試験を拡大 最大1,000台の展開を構想量産・展開
  3. 3newmoとKDDIが資本業務提携 2028年に堺市で自動運転タクシー商用化目指す提携・買収
  4. 4NVIDIA、CoRLでGR00T 2とIsaac Lab 3.0を発表新製品・発表
  5. 5UC BerkeleyとNVIDIAの研究者、AIエージェントでロボット制御コードを仮想環境で改善研究・技術

PR・アフィリエイト広告

OZ GAMING(GPU搭載BTOゲーミングPC)

OZ GAMING(GPU搭載BTOゲーミングPC)の公式広告バナーOZ GAMINGで完成品PCを見る ↗
PR本紙発行元 EmplifAI