MISO: 大規模環境における効率的でグローバルに一貫したニューラル暗黙再構成のためのマルチレゾリューションサブマップ最適化
MISO: Multiresolution Submap Optimization for Efficient Globally Consistent Neural Implicit Reconstruction
ニューラル暗黙表現を用いたSLAMにおいて、マルチレゾリューションのサブマップを階層的に最適化することで、大規模環境でも計算効率と推定精度を両立させる手法を提案した。
著者: Yulun Tian, Hanwen Cao, Sunghwan Kim, Nikolay Atanasov
分類: cs.RO
原文アブストラクト
Neural implicit representations have had a significant impact on simultaneous localization and mapping (SLAM) by enabling robots to build continuous, differentiable, and high-fidelity 3D maps from sensor data. However, as the scale and complexity of the environment increase, neural SLAM approaches face renewed challenges in the back-end optimization process to keep up with runtime requirements and maintain global consistency. We introduce MISO, a hierarchical optimization approach that leverages multiresolution submaps to achieve efficient and scalable neural implicit reconstruction. For local SLAM within each submap, we develop a hierarchical optimization scheme with learned initialization that substantially reduces the time needed to optimize the implicit submap features. To correct estimation drift globally, we develop a hierarchical method to align and fuse the multiresolution submaps, leading to substantial acceleration by avoiding the need to decode the full scene geometry. MISO significantly improves computational efficiency and estimation accuracy of neural signed distance function (SDF) SLAM on large-scale real-world benchmarks.
