Automatic Spatial Calibration of Near-Field MIMO Radar With Respect to Optical Depth Sensors
- URL: http://arxiv.org/abs/2403.10981v2
- Date: Tue, 13 Aug 2024 08:16:08 GMT
- Title: Automatic Spatial Calibration of Near-Field MIMO Radar With Respect to Optical Depth Sensors
- Authors: Vanessa Wirth, Johanna Bräunig, Danti Khouri, Florian Gutsche, Martin Vossiek, Tim Weyrich, Marc Stamminger,
- Abstract summary: We propose a novel, joint calibration approach for optical RGB-D sensors and MIMO radars that is designed to operate in the radar's near-field range.
Our pipeline consists of a bespoke calibration target, allowing for automatic target detection and localization.
We validate our approach using two different depth sensing technologies from the optical domain.
- Score: 4.328226032204419
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite an emerging interest in MIMO radar, the utilization of its complementary strengths in combination with optical depth sensors has so far been limited to far-field applications, due to the challenges that arise from mutual sensor calibration in the near field. In fact, most related approaches in the autonomous industry propose target-based calibration methods using corner reflectors that have proven to be unsuitable for the near field. In contrast, we propose a novel, joint calibration approach for optical RGB-D sensors and MIMO radars that is designed to operate in the radar's near-field range, within decimeters from the sensors. Our pipeline consists of a bespoke calibration target, allowing for automatic target detection and localization, followed by the spatial calibration of the two sensor coordinate systems through target registration. We validate our approach using two different depth sensing technologies from the optical domain. The experiments show the efficiency and accuracy of our calibration for various target displacements, as well as its robustness of our localization in terms of signal ambiguities.
Related papers
- MAROON: A Framework for the Joint Characterization of Near-Field High-Resolution Radar and Optical Depth Imaging Techniques [4.816237933371206]
We take on the unique challenge of characterizing depth imagers from both, the optical and radio-frequency domain.
We provide a comprehensive evaluation of their depth measurements with respect to distinct object materials, geometries, and object-to-sensor distances.
All object measurements will be made public in form of a multimodal dataset, called MAROON.
arXiv Detail & Related papers (2024-11-01T11:53:10Z) - SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields [10.958143040692141]
In rapidly-evolving domains such as autonomous driving, the use of multiple sensors with different modalities is crucial to ensure operational precision and stability.
To correctly exploit the provided information by each sensor in a single common frame, it is essential for these sensors to be accurately calibrated.
We leverage the ability of Neural Radiance Fields to represent different modalities in a common representation.
arXiv Detail & Related papers (2023-11-27T13:25:47Z) - Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a
Light-Weight ToF Sensor [58.305341034419136]
We present the first dense SLAM system with a monocular camera and a light-weight ToF sensor.
We propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor.
Experiments demonstrate that our system well exploits the signals of light-weight ToF sensors and achieves competitive results.
arXiv Detail & Related papers (2023-08-28T07:56:13Z) - Automated Automotive Radar Calibration With Intelligent Vehicles [73.15674960230625]
We present an approach for automated and geo-referenced calibration of automotive radar sensors.
Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles.
Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner.
arXiv Detail & Related papers (2023-06-23T07:01:10Z) - Vision Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection
in Crowds [26.129503530877006]
We develop a novel dual-sensing system, in which a vision sensor is leveraged to guide digital beamforming in a radar.
The calibrated dual system achieves about two centimeters precision in three-dimensional space within a field of view of $75circ$ by $65circ$ and for a range of two meters.
arXiv Detail & Related papers (2023-06-18T10:09:16Z) - Continuous Target-free Extrinsic Calibration of a Multi-Sensor System
from a Sequence of Static Viewpoints [0.0]
Mobile robotic applications need precise information about the geometric position of the individual sensors on the platform.
Erroneous calibration parameters have a negative impact on typical robotic estimation tasks.
We propose a new method for a continuous estimation of the calibration parameters during operation of the robot.
arXiv Detail & Related papers (2022-07-08T09:36:17Z) - Drone Detection and Tracking in Real-Time by Fusion of Different Sensing
Modalities [66.4525391417921]
We design and evaluate a multi-sensor drone detection system.
Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest.
The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution.
arXiv Detail & Related papers (2022-07-05T10:00:58Z) - Design of Sensor Fusion Driver Assistance System for Active Pedestrian
Safety [0.0]
We present a sensor fusion detection system that combines a camera and 1D light detection and ranging (lidar) sensor for object detection.
The proposed system achieves a high level of accuracy for pedestrian or object detection in front of a vehicle, and has high robustness to special environments.
arXiv Detail & Related papers (2022-01-23T08:52:32Z) - Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor
Setups [68.8204255655161]
We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras.
The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
arXiv Detail & Related papers (2021-01-12T12:02:26Z) - Learning Camera Miscalibration Detection [83.38916296044394]
This paper focuses on a data-driven approach to learn the detection of miscalibration in vision sensors, specifically RGB cameras.
Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric.
By training a deep convolutional neural network, we demonstrate the effectiveness of our pipeline to identify whether a recalibration of the camera's intrinsic parameters is required or not.
arXiv Detail & Related papers (2020-05-24T10:32:49Z) - Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment.
We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one.
Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
arXiv Detail & Related papers (2020-03-23T10:51:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.