# Find my mug: Efficient object search with a mobile robot using semantic segmentation

Source: [Find my mug: Efficient object search with a mobile robot using semantic segmentation](<https://www.sota2.com/research/paper/68fa614d1e209131ce9f38fb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:56:35.880Z

Authors: Daniel Wolf, Markus Bajones, Johann Prankl, Markus Vincze

Published: 2014-04-23T09:48:30.000Z

## Abstract

In this paper, we propose an efficient semantic segmentation framework for indoor scenes, tailored to the application on a mobile robot. Semantic segmentation can help robots to gain a reasonable understanding of their environment, but to reach this goal, the algorithms not only need to be accurate, but also fast and robust. Therefore, we developed an optimized 3D point cloud processing framework based on a Randomized Decision Forest, achieving competitive results at sufficiently high frame rates. We evaluate the capabilities of our method on the popular NYU depth dataset and our own data and demonstrate its feasibility by deploying it on a mobile service robot, for which we could optimize an object search procedure using our results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1404.5765>)

[PDF](<https://arxiv.org/pdf/1404.5765.pdf>)