A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

Electronics

Ajit Jha¹  Linga Reddy Cenkeramaddi²  Aarav Pandya³ 

[1] Department of Engineering Sciences, University of Agder, 4879 Grimstad, Norway;Department of ICT, University of Agder, 4879 Grimstad, Norway;Engineer at Deloitte USI, Jamnagar 361001, India;

关键词: velocity estimation;    optical flow;    monocular camera;    autonomous systems;    mmWave radar;   

DOI  :  10.3390/electronics10192397

来源: DOAJ

【 摘 要 】

Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow velocity estimation when the object of interest is at the edge of the frame or far away from the camera without requiring camera–radar calibration. Moreover, algorithms of various complexity were implemented using custom dataset, and each of them successfully detected the object and estimated its velocity accurately and independently of the object’s distance and location in frame. Here, we present a complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance. It includes setup design, data acquisition, dataset development, and finally, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.

【 授权许可】

Unknown