微分可能SLAMネット：視覚ナビゲーションのためのパーティクルSLAM学習
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation
パーティクルフィルタ型SLAMを微分可能な計算グラフとして実装し、ナビゲーション目的に合わせてSLAMとNN部品を一括学習。未知屋内環境のロボットナビゲーションでORB-SLAMを上回り、PointNav成功率を37%から64%へ改善。
著者: Peter Karkus, Shaojun Cai, David Hsu
分類: cs.CV, cs.AI, cs.LG, cs.RO, stat.ML
原文アブストラクト
Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments. SLAM-net encodes a particle filter based SLAM algorithm in a differentiable computation graph, and learns task-oriented neural network components by backpropagating through the SLAM algorithm. Because it can optimize all model components jointly for the end-objective, SLAM-net learns to be robust in challenging conditions. We run experiments in the Habitat platform with different real-world RGB and RGB-D datasets. SLAM-net significantly outperforms the widely adapted ORB-SLAM in noisy conditions. Our navigation architecture with SLAM-net improves the state-of-the-art for the Habitat Challenge 2020 PointNav task by a large margin (37% to 64% success). Project website: http://sites.google.com/view/slamnet
