SLAM in Environment with Glass

2021 Fiscal Year Final Research Report

• Project/Area Number: 20K21802
• Research Category: Grant-in-Aid for Challenging Research (Exploratory)
• Allocation Type: Multi-year Fund
• Review Section: Medium-sized Section 61:Human informatics and related fields
• Research Institution: The University of Tokyo
• Principal Investigator: Yamashita Atsushi 東京大学, 大学院工学系研究科(工学部), 准教授 (30334957)
• Co-Investigator(Kenkyū-buntansha): 淺間 一 東京大学, 大学院工学系研究科(工学部), 教授 (50184156)
• Project Period (FY): 2020-07-30 – 2022-03-31
• Keywords: SLAM / 地図生成 / 自己位置推定 / 透明物体 / ガラス

Outline of Final Research Achievements

This study proposes a new Simultaneous Localization and Mapping (SLAM) method in environment with glass. Accurate localization and mapping are essential for mobile robots. Using laser rangefinders (LRFs), current state-of-the-art indoor SLAM can provide accurate real-time localization and mapping in most environments. An exemption are those where glass is predominant, as LRFs can not properly detect glass due to glass' transparency and reflectiveness. With such buildings becoming more common, this has become an important issue to address. Failure to detect glass causes two problems for SLAM: incorrectly mapping glass as open space; and, lower localization accuracy due to mismatches between measured and expected range data. This study proposes a glass confidence map that correctly maps glass as occupied, as well as the probability of an object to be glass/non-glass.

Free Research Field

ロボット工学

Academic Significance and Societal Importance of the Research Achievements

ロボット分野において透明物体存在環境におけるSLAMは未解決問題である．一般的な環境では窓ガラスや自動ドアなどの透明物体が存在しており，従来のSLAM技術では地図生成や自己位置推定に失敗することが問題であった．本研究でこの問題に正面から取り組むことによって，移動ロボットの活用範囲を大幅に拡大させることが可能となった．また本研究は，障害物と移動ロボットの位置という幾何学的な情報と，障害物の表面の反射特性という物理的な特性の両者を，汎用LRFのみを用いてリアルタイムにセンシングするという斬新な枠組みを新規に提案するものであり，学術的な新規性と理論的な発展が大きい．