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Found 10 papers, 0 papers with code
Digital Self-Interference Cancellation With Robust Multi-layered Total Least Mean Squares Adaptive Filters
In each layer, our proposed m-MTLS estimator first employs an M-estimate total least mean squares (MTLS) algorithm to eliminate residual SI from the received signal and give a new estimation of the RT channel.
Overview and Performance Analysis of Various Waveforms in High Mobility Scenarios
In the high-mobility scenarios of next-generation wireless communication systems (beyond 5G/6G), the performance of orthogonal frequency division multiplexing (OFDM) deteriorates drastically due to the loss of orthogonality between the subcarriers caused by large Doppler frequency shifts.
Diff-Net: Image Feature Difference based High-Definition Map Change Detection for Autonomous Driving
Compared to traditional methods based on object detectors, the essential design in our work is a parallel feature difference calculation structure that infers map changes by comparing features extracted from the camera and rasterized images.
Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization
Detailed ablation and visualization analysis are included to further demonstrate each of our proposed modules' effectiveness in our method.
Autonomous Driving
Data Augmentation
+1
Robotics
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images
In this paper, we first introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks through an analysis of the imaging process, then propose a Semantic Object Segmentation and Depth Estimation Network (SOSD-Net) based on the objectness assumption.
Ranked #81 on
Semantic Segmentation
on NYU Depth v2
DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving
We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy.
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods.
Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes
We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes.
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes
Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.
Robust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving
Experiments on the KITTI dataset demonstrate the accuracy of our ground plane estimation, monocular SFM and object localization relative to ground truth, with detailed comparisons to prior art.
