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This paper discusses an indoor robotic system that integrates a state-of-the-art object detection algorithm trained with data augmented for an indoor scenario and enabled with mechanisms to localize and position objects in 3D and display them interactively to a user. Size, weight, and power constraints in a mobile robot constrain the type of computing hardware that can be integrated with the robotic platform. However, on the other hand, the robot’s mobility if leveraged properly can provide enough opportunity to detect objects from different distances and viewpoints as the robot approaches them giving more robust results. This work adapts a CNN-based algorithm, YOLO, to run on a GPU-enabled board, the Jetson TX1. An innovative method to calculate the object position in the 3D environment map is discussed along with the problems therein, such as that of duplicate detections that need to be suppressed. Since multiple objects of different or same class may be detected, the user is overloaded with information and management of the visualization through human–machine interaction gains an important role. A scheme for informative display of objects is implemented which lets the user interactively view object images as well as their position in the scene. The complete robotic system including the interactive visualization tool can be put to various uses such as search and rescue, indoor assistance, patrolling and surveillance.
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- Visual Object Detection for an Autonomous Indoor Robotic System
Anima M. Sharma
Imran A. Syed
- Springer Singapore