No abstract

We aim to improve the quality of time spent in co-located social interactions by encouraging people to limit their smartphone usage together. We present a prototype called Lock n' LoL, an app that allows co-located users to lock their smartphones and limit their usage by enforcing users to ask for explicit use permission. From our preliminary study, we designed two modes to deal with the dynamics of smartphone use during the co-located social interactions: (1) socializing mode (i.e., locking smartphones to limit usage together) and (2) temporary use mode (i.e., requesting/granting temporary smartphone use). We conducted a pilot study (n = 20) with our working prototype, and the results documented the helpfulness of Lock n' LoL when used in socializing.

Perception of traversable regions and objects of interest from a 3D point cloud is one of the critical tasks in autonomous navigation. A ground vehicle needs to look for traversable terrains that are explorable by wheels. Then, to make safe navigation decisions, the segmentation of objects positioned on those terrains has to be followed up. However, over-segmentation and under-segmentation can negatively influence such navigation decisions. To that end, we propose TRAVEL, which performs traversable ground detection and object clustering simultaneously using the graph representation of a 3D point cloud. To segment the traversable ground, a point cloud is encoded into a graph structure, tri-grid field, which treats each tri-grid as a node. Then, the traversable regions are searched and redefined by examining local convexity and concavity of edges that connect nodes. On the other hand, our above-ground object segmentation employs a graph structure by representing a group of horizontally neighboring 3D points in a spherical-projection space as a node and vertical/horizontal relationship between nodes as an edge. Fully leveraging the node-edge structure, the above-ground segmentation ensures real-time operation and mitigates over-segmentation. Through experiments using simulations, urban scenes, and our own datasets, we have demonstrated that our proposed traversable ground segmentation algorithm outperforms other state-ofthe-art methods in terms of the conventional metrics and that our newly proposed evaluation metrics are meaningful for assessing the above-ground segmentation. We will make the code and our own dataset available to public at https://github.com/url-kaist/TRAVEL.