Find my mug: Efficient object search with a mobile robot using semantic segmentation
This work addresses the need for fast and robust semantic segmentation to enable mobile robots to efficiently search for objects in indoor environments, representing an incremental improvement.
The researchers tackled efficient semantic segmentation for mobile robots by developing an optimized 3D point cloud framework based on a Randomized Decision Forest, achieving competitive results at high frame rates on the NYU depth dataset and custom data, and demonstrated its feasibility by deploying it on a service robot to optimize object search.
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.