Automated System for Semantic Object Labeling With Soft-Object Recognition and Dynamic Programming Segmentation

Abstract

This paper presents an automated robotic system for generating semantic maps of inventory in retail environments. In retail settings, semantic maps are labeled maps of stores where each discrete section of shelving is assigned a department label describing the types of products on that shelf. Starting from a metric map of the store, the robot autonomously extracts the shelf boundaries, generates a distance-optimal tour of the store to view every shelf, and follows the tour while avoiding unmapped clutter and moving people. The robot creates a point cloud of the store using the data collected from this tour. We introduce a novel soft-object assignment algorithm to create a virtual map and a dynamic programming algorithm to segment this map. These algorithms use a priori information about the products to boost data from laser and camera sensors in order to recognize and semantically label objects. The primary contribution of this paper is the integration of multiple systems for automated path planning, navigation, object recognition, and semantic mapping. This paper represents an important contribution toward deploying mobile robots in dynamic human environments.