Automotive radar gridmap representations

Werber, Klaudius; Rapp, Matthias; Klappstein, Jens; Hahn, Markus; Dickmann, Jürgen; Dietmayer, Klaus; Waldschmidt, Christian

doi:10.1109/icmim.2015.7117922

Cited by 73 publications

(45 citation statements)

References 8 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wöhler et al [7] extract stochastic features and use them as input for a random forest classifier and a long short-term memory network. In order to classify static objects, Lombacher et al [8] accumulate raw radar data over time and transform them into a grid map representation [9]. Then, windows around potential objects are cut out and used as input for a deep neural network.…”

Section: Related Workmentioning

confidence: 99%

2D Car Detection in Radar Data with PointNets

Danzer

Griebel

Bach

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Self Cite

128

View full text Add to dashboard Cite

For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.

show abstract

Section: Related Workmentioning

confidence: 99%

2D Car Detection in Radar Data with PointNets

Danzer

Griebel

Bach

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Self Cite

128

View full text Add to dashboard Cite

show abstract

“…RCS is a parameter to measure the ability of a target to reflect electromagnetic waves. For the same millimeter wave radar, it is related to the material, geometric shape, and observation angle of the target [ 46 ]. In other words, the RCS can reflect the characteristics of the target stably when the observation angle is constant.…”

Section: Tight Coupling Of Localization and Mappingmentioning

confidence: 99%

“…It was also necessary to construct the map M before performing point cloud matching. The authors of [ 46 ] constructed amplitude grid map, and the map here is the same in essence. The value of each grid M x , y a is the echo power of the target, expressed as A ( M x , y )( t ).…”

Section: Tight Coupling Of Localization and Mappingmentioning

confidence: 99%

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Liu

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into “Multi-scan,” the problem of small data volume is solved. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a “Scan to Map” point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. Based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios.

show abstract

“…In most, if not all, related work it is common to see a use of non-public data sets. Moreover, due to the lack of data, occupancy grid results are often demonstrated only qualitatively on few samples [25], [13], [20], making it more difficult to compare between different published results. Other studies suggest measuring distance errors for specific scenarios [5], or ROC curve [11].…”

Section: Related Workmentioning

confidence: 99%

“…In addition to identifying occupied and free cells, the inability to observe a known state of cells is also an important concept in occupancy grid mapping and has been addressed in several ways. In cases where each cell is associated with an occupancy probability, such as in [25], [6], [18], a probability of 0.5 represents the highest uncertainty between occupied and free, and is equivalent to having no knowledge of a cell's occupancy state. Unobserved state can also be leveraged in inverse sensor modeling [20].…”

Section: Related Workmentioning

confidence: 99%

Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation

Sless

Shlomo

Cohen

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

View full text Add to dashboard Cite

Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches. In this work, we propose learning the inverse sensor model used for occupancy grid mapping from clustered radar data. This is done in a data driven approach that leverages computer vision techniques. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data.

show abstract

Automotive radar gridmap representations

Cited by 73 publications

References 8 publications

2D Car Detection in Radar Data with PointNets

2D Car Detection in Radar Data with PointNets

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation

Contact Info

Product

Resources

About