Object detection and mapping for service robot tasks

Abstract

The problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection/recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone.Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

Conceptual spatial representations for indoor mobile robots

“…In an attempt to allow for use of smaller objects, like cups, books, etc we applied a second method which is based on the ideas presented in [32]. One of the key points here is that object recognition, when performed on a mobile platform, actually should be thought of as two separate processes, object detection and object recognition.…”

“…This makes the acquisition of knowledge about new objects more complicated. In [32], it is suggested to use image differencing combined with morphological operations to segment the image. For this to work the user has to present the robot with two images, one with and one without the objects.…”

Not seeing is also believing: Combining object and metric spatial information

“…These hybrid representations have been successfully applied in tasks such as navigation ( [3]). A related field that has been growing recently is semantic mapping (e.g., [4], [5], [6], [7]), where typically the focus is to endow topological regions of space with semantic attributes, such as in the task of place classification. Topological and semantic information is typically extracted from metric layers (occupancy grids).…”

Semi-task-dependent and uncertainty-driven world model maintenance

“…Because of their focus on maps used for localization and navigation instead of semantic object maps (Milford and Wyeth 2010;Churchill and Newman 2012;Konolige and Bowman 2009), and the references therein, are considered to be complementary to the proposed approach rather than being alternatives. In Ekvall et al (2007), knowledge about object poses is incorporated in a SLAM approach, however, due to the computational complexity only ten objects are tracked. This is considered insufficient for real world environments.…”