深層慣性測位のための学習可能な時空間マップ埋め込み
Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization
屋内測位において、学習した空間マップ埋め込みと時間的オドメトリ埋め込みを組み合わせることで、ユーザーの取り得る位置のデータ駆動型事前分布を構築し、慣性のみの測位精度を49%向上させた。
著者: Dennis Melamed, Karnik Ram, Vivek Roy, Kris Kitani
分類: cs.RO, cs.CV
原文アブストラクト
Indoor localization systems often fuse inertial odometry with map information via hand-defined methods to reduce odometry drift, but such methods are sensitive to noise and struggle to generalize across odometry sources. To address the robustness problem in map utilization, we propose a data-driven prior on possible user locations in a map by combining learned spatial map embeddings and temporal odometry embeddings. Our prior learns to encode which map regions are feasible locations for a user more accurately than previous hand-defined methods. This prior leads to a 49% improvement in inertial-only localization accuracy when used in a particle filter. This result is significant, as it shows that our relative positioning method can match the performance of absolute positioning using bluetooth beacons. To show the generalizability of our method, we also show similar improvements using wheel encoder odometry.
