Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle

TL;DR

This paper introduces a real-time, low-cost vision-based system for accurately detecting keypoints and estimating the pose of an unmanned ground vehicle to facilitate autonomous recovery in rescue scenarios.

Contribution

It presents a novel lightweight neural network, UGV-KPNet, for real-time keypoint detection, and creates the first large-scale dataset for UGV keypoints, enhancing pose estimation accuracy.

Findings

Achieves state-of-the-art accuracy in keypoint detection

Operates in real-time with low computational cost

Improves 6-DoF pose estimation for UGVs

Abstract

The combination of a small unmanned ground vehicle (UGV) and a large unmanned carrier vehicle allows more flexibility in real applications such as rescue in dangerous scenarios. The autonomous recovery system, which is used to guide the small UGV back to the carrier vehicle, is an essential component to achieve a seamless combination of the two vehicles. This paper proposes a novel autonomous recovery framework with a low-cost monocular vision system to provide accurate positioning and attitude estimation of the UGV during navigation. First, we introduce a light-weight convolutional neural network called UGV-KPNet to detect the keypoints of the small UGV from the images captured by a monocular camera. UGV-KPNet is computationally efficient with a small number of parameters and provides pixel-level accurate keypoints detection results in real-time. Then, six degrees of freedom pose is estimated using the detected keypoints to obtain positioning and attitude information of the UGV. Besides, we are the first to create a large-scale real-world keypoints dataset of the UGV. The experimental results demonstrate that the proposed system achieves state-of-the-art performance in terms of both accuracy and speed on UGV keypoint detection, and can further boost the 6-DoF pose estimation for the UGV.