EnforceNet：大規模屋内スパースLiDAR点群における単眼カメラ自己位置推定
EnforceNet: Monocular Camera Localization in Large Scale Indoor Sparse LiDAR Point Cloud
スパースなLiDAR地図上で単眼RGBカメラの位置をセンチメートル精度で推定するニューラルネットを提案。汎化・精度・収束を改善する「抵抗モジュール」を導入し、屋内駐車場データで検証した。
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
Pose estimation is a fundamental building block for robotic applications such as autonomous vehicles, UAV, and large scale augmented reality. It is also a prohibitive factor for those applications to be in mass production, since the state-of-the-art, centimeter-level pose estimation often requires long mapping procedures and expensive localization sensors, e.g. LiDAR and high precision GPS/IMU, etc. To overcome the cost barrier, we propose a neural network based solution to localize a consumer degree RGB camera within a prior sparse LiDAR map with comparable centimeter-level precision. We achieved it by introducing a novel network module, which we call resistor module, to enforce the network generalize better, predicts more accurately, and converge faster. Such results are benchmarked by several datasets we collected in the large scale indoor parking garage scenes. We plan to open both the data and the code for the community to join the effort to advance this field.
