A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

Kung, Pou-Chun; Wang, Chieh-Chih; Lin, Wen‐Chieh

doi:10.48550/arxiv.2103.07908

Cited by 7 publications

(9 citation statements)

References 31 publications

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“…Some more recent works include [ 136 ], which extracts the Binary Annular Statistics Descriptor (BASD) for feature matching, and then performs graph optimization; and [ 137 ], where SURF and M2DP [ 96 ] descriptors are computed from radar point clouds for feature association and loop-closure detection, respectively; as well as the use of SIFT in [ 138 ]. Radar measurements are noisy and, thus, may worsen the performance of scan-matching algorithms used for LiDAR, such as ICP and NDT; nevertheless, G-ICP [ 70 ] showed good validity in [ 139 ], where the covariance of each measurement was assigned according to its range; the same can be said of NDT in [ 140 ] and GMM in [ 141 ], which incorporated detection clustering algorithms including k-means, DBSCAN, and OPTICS. In [ 142 ], Radar Cross-Section (RCS) was used as a cue for assisting with feature extraction and Correlative Scan Matching (CSM).…”

Section: Sensors and Sensor-based Odometry Methodsmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

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Section: Sensors and Sensor-based Odometry Methodsmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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“…Overall Performance on nuScenes 1) Competing approaches: We compare our approach with 4 SOTA radar metric localization methods, including direct methods (convention ICP [14] and submap NDT [24]), the learning-based method (UTR [2]), and the feature-based method (MC-RANSAC [5]). Note that the SOTA joint-Doppler-based NDT approach [6] is designed for odometry rather than metric localization. Thus we implement a gridbased NDT [24] for fair comparisons.…”

Section: Evaluation a Experimental Setupmentioning

confidence: 99%

“…For readability, we refer to such radars as automotive radars hereafter. As opposed to the mechanical spinning radar [6] that can only provide ranging and intensity information, automotive radars (c.f. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…However, along with the advantages of automotive radars come with low-quality scans. It has been found that this type of radar suffers from limited-angle resolution, higher noise floor, and sparser point cloud density than the mechanical spinning radar [6]. The low-quality radar scans, however, threaten the feature association between two scans and undermine the reliability of metric localization when using automotive radars.…”

Section: Introductionmentioning

confidence: 99%

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DC-Loc: Accurate Automotive Radar Based Metric Localization with Explicit Doppler Compensation

Gao¹,

Zhang²,

Wang³

et al. 2021

Preprint

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Automotive mmWave radar has been widely used in the automotive industry due to its small size, low cost, and complementary advantages to optical sensors (e.g., cameras, LiDAR, etc.) in adverse weathers, e.g., fog, raining, and snowing. On the other side, its large wavelength also poses fundamental challenges to perceive the environment. Recent advances have made breakthroughs on its inherent drawbacks, i.e., the multipath reflection and the sparsity of mmWave radar's point clouds. However, the frequency-modulated continuous wave modulation of radar signals makes it more sensitive to vehicles' mobility than optical sensors. This work focuses on the problem of frequency shift, i.e., the Doppler effect distorts the radar ranging measurements and its knock-on effect on metric localization. We propose a new radar-based metric localization framework that obtains more accurate location estimation by restoring the Doppler distortion. Specifically, we first design a new algorithm that explicitly compensates the Doppler distortion of radar scans and then model the measurement uncertainty of the Doppler-compensated point cloud to further optimize the metric localization. Extensive experiments using the public nuScenes dataset and CARLA simulator demonstrate that our method outperforms the stateof-the-art approach by 25.2% and 5.6% improvements in terms of translation and rotation errors, respectively.

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“…These unique characteristics make automotive radar an attractive sensor for autonomous driving. While prior works use them to estimate the vehicle's (ego-)motion [17]- [19], realizing robust place recognition by automotive radar has never been explored and features different challenges. Compared with the output of a spinning radar, the automotive radar point clouds are much noisier and sparser due to the multi-path effects and the fundamental millimeter-wave diffraction effects [9].…”

Section: Introductionmentioning

confidence: 99%

AutoPlace: Robust Place Recognition with Single-chip Automotive Radar

Cai¹,

Wang²,

Lu³

2021

Preprint

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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 Section measurement. Extensive experimental results on the public nuScenes dataset demonstrate that existing visual/LiDAR/spinning radar place recognition approaches are less suitable for single-chip automotive radar. In contrast, our purpose-built approach for automotive radar consistently outperforms a variety of baseline methods via a comprehensive set of metrics, providing insights into the efficacy when used in a realistic system.

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A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

Cited by 7 publications

References 31 publications

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

DC-Loc: Accurate Automotive Radar Based Metric Localization with Explicit Doppler Compensation

AutoPlace: Robust Place Recognition with Single-chip Automotive Radar

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