Isingプロセスモデルによる連続占有マップの学習
Learning Continuous Occupancy Maps with the Ising Process Model
Isingモデルを一般化して連続的な占有場をモデル化し、少数のハイパーパラメータを擬似尤度最大化で学習する計算効率の良いロボットナビゲーション用地図構築手法を提案した。
著者: Nicholas O'Dell, Christopher Renton, Adrian Wills
分類: cs.RO, stat.ML
原文アブストラクト
We present a new method of learning a continuous occupancy field for use in robot navigation. Occupancy grid maps, or variants of, are possibly the most widely used and accepted method of building a map of a robot's environment. Various methods have been developed to learn continuous occupancy maps and have successfully resolved many of the shortcomings of grid mapping, namely, priori discretisation and spatial correlation. However, most methods for producing a continuous occupancy field remain computationally expensive or heuristic in nature. Our method explores a generalisation of the so-called Ising model as a suitable candidate for modelling an occupancy field. We also present a unique kernel for use within our method that models range measurements. The method is quite attractive as it requires only a small number of hyperparameters to be trained, and is computationally efficient. The small number of hyperparameters can be quickly learned by maximising a pseudo likelihood. The technique is demonstrated on both a small simulated indoor environment with known ground truth as well as large indoor and outdoor areas, using two common real data sets.
