移動物体追跡評価のための協調グリッドマッピング
Collaborative Grid Mapping for Moving Object Tracking Evaluation
複数のLiDAR搭載車両と路側センサのデータをグリッドマップ上で協調融合し、動的物体追跡の疑似正解データを半自動生成する手法を提案・評価した。
著者: Rémy Huet, Antoine Lima, Philippe Xu, Véronique Cherfaoui, Philippe Bonnifait
分類: cs.RO
原文アブストラクト
Perception of other road users is a crucial task for intelligent vehicles. Perception systems can use on-board sensors only or be in cooperation with other vehicles or with roadside units. In any case, the performance of perception systems has to be evaluated against ground-truth data, which is a particularly tedious task and requires numerous manual operations. In this article, we propose a novel semi-automatic method for pseudo ground-truth estimation. The principle consists in carrying out experiments with several vehicles equipped with LiDAR sensors and with fixed perception systems located at the roadside in order to collaboratively build reference dynamic data. The method is based on grid mapping and in particular on the elaboration of a background map that holds relevant information that remains valid during a whole dataset sequence. Data from all agents is converted in time-stamped observations grids. A data fusion method that manages uncertainties combines the background map with observations to produce dynamic reference information at each instant. Several datasets have been acquired with three experimental vehicles and a roadside unit. An evaluation of this method is finally provided in comparison to a handmade ground truth.
