In this paper, a probabilistic approach is proposed for semantic classification in indoor environments using laser range data. Robot locations in indoor environments are categorized into three broad classes as room, corridor, and door. K-means and Learning Vector Quantization (LVQ) methods are used to classify robot positions. Circular shifting is applied to render laser range data independent of robot pose. K-means or LVQ algorithms are used to determine data clusters and their centers. In K-means method, the cluster centers are modelled with the proposed probabilistic approach to consider the semantic class of robot location. On the other hand, LVQ method inherently provides semantic classes of the cluster centers. In order to improve the rate of classification success, Markov model is integrated into the proposed approach. Experiments are conducted to demonstrate the effectiveness of the proposed approach. The results indicate that K-means method successfully classifies rooms and corridors, but door classification success rate is not satisfactory. LVQ method improves door classification rate without decreasing the classification rate of corridor and room. Lastly, effectiveness of the Markov model is discussed.

In this work, the use of t-SNE is proposed to embed 3D point clouds of plants into 2D space for plant characterization. It is demonstrated that t-SNE operates as a practical tool to flatten and visualize a complete 3D plant model in 2D space. The perplexity parameter of t-SNE allows 2D rendering of plant structures at various organizational levels. Aside from the promise of serving as a visualization tool for plant scientists, t-SNE also provides a gateway for processing 3D point clouds of plants using their embedded counterparts in 2D. In this paper, simple methods were proposed to perform semantic segmentation and instance segmentation via grouping the embedded 2D points. The evaluation of these methods on a public 3D plant data set conveys the potential of t-SNE for enabling 2D implementation of various steps involved in automatic 3D phenotyping pipelines.