Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions

Cen, Sarah H.; Newman, Paul

doi:10.1109/icra.2018.8460687

Cited by 173 publications

(190 citation statements)

References 13 publications

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“…Although the error seemingly increases for more complex environments (e.g., in Iceland), these discrepancies may be due to factors discussed in the previous paragraph. This work achieves lower error in Oxford city compared to our previous work [2]. Though not shown, it consistently outperforms [2], which cannot handle unstructured settings.…”

Section: Resultsmentioning

confidence: 59%

“…It requires multiple tunable parameters, provides redundant returns, and often struggles with bright patches caused by RPC. In comparison to our previous work [2], our new algorithm has fewer parameters and operates well even in unstructured environments, like forests.…”

Section: B Keypoint Extractionmentioning

confidence: 92%

“…In this section, we present an improved data association algorithm, which is revised from [2] and designed to:…”

Section: Data Associationmentioning

confidence: 99%

“…Proof: By the definition of compatibility scores [2], the maximum score C * ij = 1 if i, j ∈ M * , and 0 ≤ C * ij < 1 otherwise. Since we assume perfect measurements and no symmetries, to meet the uniqueness constraint in (1), m * i = 1 ⇒ C * ij = 1∀j ∈ {1, .…”

Section: B Theoretical Analysis and Performance Measuresmentioning

confidence: 99%

“…We present a new termination condition that stops adding matches when the new one is incompatible with those already in M , as explained in Section IV-B. Compared to our previous work [2], the new condition is more robust and consistent because it does not depend on the magnitude of elements in v * . The rigid-body motion (RBM) is found using a wellknown technique [25].…”

mentioning

confidence: 99%

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Radar-only ego-motion estimation in difficult settings via graph matching

Cen

Newman

2019

2019 International Conference on Robotics and Automation (ICRA)

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Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.

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Section: Resultsmentioning

confidence: 59%

Section: B Keypoint Extractionmentioning

confidence: 92%

“…In this section, we present an improved data association algorithm, which is revised from [2] and designed to:…”

Section: Data Associationmentioning

confidence: 99%

Section: B Theoretical Analysis and Performance Measuresmentioning

confidence: 99%

mentioning

confidence: 99%

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Radar-only ego-motion estimation in difficult settings via graph matching

Cen

Newman

2019

2019 International Conference on Robotics and Automation (ICRA)

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FMCW Radar on LiDAR map localization in structural urban environments

Ma,

Li,

Zhao

et al. 2024

Journal of Field Robotics

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Multisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle.

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Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog

Kramer

Heckman

2021

Springer Proceedings in Advanced Robotics

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Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions

Cited by 173 publications

References 13 publications

Radar-only ego-motion estimation in difficult settings via graph matching

Radar-only ego-motion estimation in difficult settings via graph matching

FMCW Radar on LiDAR map localization in structural urban environments

Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog

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