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Found 9 papers, 2 papers with code
Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution
We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources.
Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras.
Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines
We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle.
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations
In particular, we fuse learned features from complementary representations.
Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function
Since a 3D surface can be usually observed from multiple camera images with different view poses, an optimal image patch selection for the texturing and an optimal semantic class estimation for the semantic mapping are still challenging.
TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis
To calibrate the stereo camera, a photometric error function is builded and the LiDAR depth is involved to transform key points from one camera to another.
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
At the heart of all automated driving systems is the ability to sense the surroundings, e. g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progress due to the release of large datasets such as SemanticKITTI and nuScenes-LidarSeg.
PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data
The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios.
Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data
This representation allows us to use well-studied deep learning architectures from the image domain to predict a dense semantic grid map using only the sparse input data of a single LiDAR scan.
