Search Results for author:
Found 11 papers, 1 papers with code
Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation
Safety-critical applications like autonomous driving call for robust 3D environment perception algorithms which can withstand highly diverse and ambiguous surroundings.
On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation
We propose a metric to measure the confidence calibration quality of a semantic segmentation model with respect to individual classes.
Survey on LiDAR Perception in Adverse Weather Conditions
The active LiDAR sensor is able to create an accurate 3D representation of a scene, making it a valuable addition for environment perception for autonomous vehicles.
On the calibration of underrepresented classes in LiDAR-based semantic segmentation
The calibration of deep learning-based perception models plays a crucial role in their reliability.
CNN-based Lidar Point Cloud De-Noising in Adverse Weather
Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors.
Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling
In this paper we propose a new CNN architecture for the point-wise semantic labeling of LiDAR data which achieves state-of-the-art results while increasing portability across sensor types.
Improved Semantic Stixels via Multimodal Sensor Fusion
Our results indicate that the proposed mid-level fusion of LiDAR and camera data improves both the geometric and semantic accuracy of the Stixel model significantly while reducing the computational overhead as well as the amount of generated data in comparison to using a single modality on its own.
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation
The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset.
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis)
The orientation extraction based on the Convolutional Neural Network shows a better performance in comparison to an orientation extraction directly over the velocity information of the dynamic occupancy grid map.
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications.
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera.
