Corpus ID: 237562926

AutoPlace: Robust Place Recognition with Low-cost Single-chip Automotive Radar

@article{Cai2021AutoPlaceRP,
  title={AutoPlace: Robust Place Recognition with Low-cost Single-chip Automotive Radar},
  author={Kaiwen Cai and Bing Wang and Chris Xiaoxuan Lu},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.08652}
}
This paper presents a novel place recognition approach to autonomous vehicles by using low-cost, single-chip automotive radar. Aimed at improving recognition robustness and fully exploiting the rich information provided by this emerging automotive radar, our approach follows a principled pipeline that comprises (1) dynamic points removal from instant Doppler measurement, (2) spatial-temporal feature embedding on radar point clouds, and (3) retrieved candidates refinement from Radar Cross… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 37 REFERENCES
Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance
This paper details an application which yields significant improvements to the adeptness of place recognition with Frequency-Modulated Continuous-Wave scanning, 360-degrees field of view radar - aExpand
RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications
TLDR
A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented to enable the development of novel (machine learning-based) radar perception algorithms with the focus on moving road users. Expand
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning
TLDR
The utility of the proposed method is analysed via a comprehensive set of metrics which provide insight into the efficacy when used in a realistic system, showing improved performance over the root architecture even in the face of random rotational perturbation. Expand
A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars
TLDR
The proposed radar odometry method adapts to both scanning and automotive radars, where the pipeline consists of thresholding, probabilistic submap building, and an Normal Distribution Transform-based (NDT-based) radar scan matching. Expand
RadarSLAM: Radar based Large-Scale SLAM in All Weathers
TLDR
RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments, composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Expand
Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar
  • Dan Barnes, I. Posner
  • Computer Science
  • 2020 IEEE International Conference on Robotics and Automation (ICRA)
  • 2020
TLDR
A self-supervised framework capable of full mapping and localisation with radar in urban environments, and is sensor agnostic and can be applied to most modalities. Expand
See through smoke: robust indoor mapping with low-cost mmWave radar
This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments toExpand
nuScenes: A Multimodal Dataset for Autonomous Driving
Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as objectExpand
Depth Estimation from Monocular Images and Sparse Radar Data
TLDR
This paper gives a comprehensive study of the fusion between RGB images and Radar measurements from different aspects and proposed a working solution based on the observations, and demonstrates that the method outperforms existing fusion methods. Expand
The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
TLDR
The target application is autonomous vehicles where this modality is robust to environmental conditions such as fog, rain, snow, or lens flare, which typically challenge other sensor modalities such as vision and LIDAR. Expand
...
1
2
3
4
...