Online Object Detection and Localization on Stereo Visual SLAM System

PIRE, TAIHÚ; CORTI, JAVIER; GRINBLAT, GUILLERMO

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS

SPRINGER

Lugar: New York; Año: 2020 vol. 98 p. 377 - 386

0921-0296

In order to navigate an unknown environment, an autonomous robot must be able to build a map of its surroundings while estimating its position at the same time. This problem is known as SLAM. We propose a SLAM system for stereo cameras which builds a map of objects in a scene. The system is based on the SLAM method S-PTAM and an object detection module. The object detection module uses Deep Learning to perform online detection and provide the 3d pose estimations of objects present in an input image, while S-PTAM estimates the camera pose in real time. The system was tested on a real world environment, achieving good object localization results.