遷移グリッドマップ：静的・動的占有の同時モデリング
Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy
センサデータから環境の静的部分と動的部分を同時に推定するベイズネットワークベースの確率的枠組みを提案し、効率的な解析解である遷移グリッドマップを導出した。実データでSLAM改善にも寄与することを示した。
著者: José Manuel Gaspar Sánchez, Leonard Bruns, Jana Tumova, Patric Jensfelt, Martin Törngren
分類: cs.RO
原文アブストラクト
Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.
