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Found 12 papers, 1 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.
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation
Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information.
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.
Learned Enrichment of Top-View Grid Maps Improves Object Detection
We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input.
Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setups
We present our approach to unsupervised domain adaptation for single-stage object detectors on top-view grid maps in automated driving scenarios.
Localization in Aerial Imagery with Grid Maps using LocGAN
In this work, we present LocGAN, our localization approach based on a geo-referenced aerial imagery and LiDAR grid maps.
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences
We apply our approach to optical flow estimation from camera image sequences, validate on odometry estimation and suggest a method to iteratively increase optical flow estimation accuracy using the generated motion masks.
Capturing Object Detection Uncertainty in Multi-Layer Grid Maps
Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks.
Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
A detailed environment perception is a crucial component of automated vehicles.
An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps
In this paper, we present a new approach to vehicle trajectory prediction based on automatically generated maps containing statistical information about the behavior of traffic participants in a given area.
