LED状態分類によるポーズラベル不要の自己教師あり視覚ポーズ推定
Self-supervised Learning Of Visual Pose Estimation Without Pose Labels By Classifying LED States
複数のLEDを搭載したロボットの単眼RGB画像から、ポーズラベルや形状知識なしに自己教師あり学習で相対ポーズを推定する手法を提案。
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著者: Nicholas Carlotti, Mirko Nava, Alessandro Giusti
分類: cs.RO
原文アブストラクト
We introduce a model for monocular RGB relative pose estimation of a ground robot that trains from scratch without pose labels nor prior knowledge about the robot's shape or appearance. At training time, we assume: (i) a robot fitted with multiple LEDs, whose states are independent and known at each frame; (ii) knowledge of the approximate viewing direction of each LED; and (iii) availability of a calibration image with a known target distance, to address the ambiguity of monocular depth estimation. Training data is collected by a pair of robots moving randomly without needing external infrastructure or human supervision. Our model trains on the task of predicting from an image the state of each LED on the robot. In doing so, it learns to predict the position of the robot in the image, its distance, and its relative bearing. At inference time, the state of the LEDs is unknown, can be arbitrary, and does not affect the pose estimation performance. Quantitative experiments indicate that our approach: is competitive with SoA approaches that require supervision from pose labels or a CAD model of the robot; generalizes to different domains; and handles multi-robot pose estimation.
