繰り返し構造下での視覚的自己位置推定における姿勢選択失敗からの回復
Recovering from Pose-Selection Failures in Repeated Visual Localization
過去の自己位置推定結果を利用して、繰り返し構造により誤った姿勢候補が選択される問題を修正する手法HPRを提案し、HLocなどで成功率を向上させた。
著者: Jaehyun Kim, Wonseok Kang, Tae-Wan Kim
分類: cs.CV, cs.RO
原文アブストラクト
Repetitive structures can give an incorrect pose candidate more inlier support than an accurate one, causing visual localization to select it. History-Guided Pose Reselection (HPR) uses coarse position forecasts from up to five past localization outputs to distinguish well-separated current-query candidates. The best-fitting forecast proposes an alternative; HPR accepts it only if every well-fitting forecast is much closer to it than to the localizer's pose. HPR needs no odometry and returns the selected candidate unchanged, using prediction for selection rather than pose refinement. With one development-set threshold, HPR improves recall within 5 meters and 10 degrees in all 12 main configurations of Hierarchical Localization (HLoc), MASt3R-based pose generation, and JIST-based retrieval, losing successes on fewer than 0.4 percent of queries. On NAVER Gangnam B1, HPR corrects 97 HLoc failures and loses six successes; gains persist within 0.25 meters and 2 degrees on all three HLoc test sets. Median HPR runtime on HLoc candidates is below one millisecond, excluding pose generation. Recovery weakens under less frequent localization.