  This paper addresses the problem of cross-season visual place classification
(VPC) from a novel perspective of long-term map learning. Our goal is to enable
transfer learning efficiently from one season to the next, at a small constant
cost, and without wasting the robot's available long-term-memory by memorizing
very large amounts of training data. To realize a good tradeoff between
generalization and specialization abilities, we employ an ensemble of
convolutional neural network (DCN) classifiers and consider the task of
scheduling (when and which classifiers to retrain), given a previous season's
DCN classifiers as the sole prior knowledge. We present a unified framework for
retraining scheduling and discuss practical implementation strategies.
Furthermore, we address the task of partitioning a robot's workspace into
places to define place classes in an unsupervised manner, rather than using
uniform partitioning, so as to maximize VPC performance. Experiments using the
publicly available NCLT dataset revealed that retraining scheduling of a DCN
classifier ensemble is crucial and performance is significantly increased by
using planned scheduling.
