A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation

10/03/2018
by   Lukas Hoyer, et al.
0

External localization is an essential part for the indoor operation of small or cost-efficient robots, as they are used, for example, in swarm robotics. We introduce a two-stage localization and instance identification framework for arbitrary robots based on convolutional neural networks. Object detection is performed on an external camera image of the operation zone providing robot bounding boxes for an identification and orientation estimation convolutional neural network. Additionally, we propose a process to generate the necessary training data. The framework was evaluated with 3 different robot types and various identification patterns. We have analyzed the main framework hyperparameters providing recommendations for the framework operation settings. We achieved up to 98 with a frame rate of 50 Hz on a GPU.

READ FULL TEXT
research
04/19/2021

LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network

Being capable of estimating the pose of uncooperative objects in space h...
research
11/07/2019

Model Adaption Object Detection System for Robot

How to detect the object and guide the robot to get close to the object ...
research
10/22/2021

CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification

Mobile autonomous robots include numerous sensors for environment percep...
research
03/20/2019

GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments

Recent advancements have led to a proliferation of machine learning syst...
research
06/20/2017

Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots while Playing Soccer

The main goal of this paper is to analyze the general problem of using C...
research
09/08/2020

Adapted Pepper

One of the main issue in robotics is the lack of embedded computational ...

Please sign up or login with your details

Forgot password? Click here to reset