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Found 5 papers, 2 papers with code
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards
In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, which is considered a key modality for robustness.
Probabilistic Approach for Road-Users Detection
It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives.
Multispectral Vineyard Segmentation: A Deep Learning approach
In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods.
Probabilistic Object Classification using CNN ML-MAP layers
Deep networks are currently the state-of-the-art for sensory perception in autonomous driving and robotics.
High-resolution LIDAR-based Depth Mapping using Bilateral Filter
High resolution depth-maps, obtained by upsampling sparse range data from a 3D-LIDAR, find applications in many fields ranging from sensory perception to semantic segmentation and object detection.
