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Found 10 papers, 2 papers with code
OSM vs HD Maps: Map Representations for Trajectory Prediction
While High Definition (HD) Maps have long been favored for their precise depictions of static road elements, their accessibility constraints and susceptibility to rapid environmental changes impede the widespread deployment of autonomous driving, especially in the motion forecasting task.
Single RGB-D Camera Teleoperation for General Robotic Manipulation
We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device.
Pose Estimation of Specular and Symmetrical Objects
In the robotic industry, specular and textureless metallic components are ubiquitous.
Auto-calibration Method Using Stop Signs for Urban Autonomous Driving Applications
Calibration of sensors is fundamental to robust performance for intelligent vehicles.
Probabilistic Semantic Mapping for Urban Autonomous Driving Applications
Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate.
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach.
Semantic Instance Labeling Leveraging Hierarchical Segmentation
Most of the approaches for indoor RGBD semantic la- beling focus on using pixels or superpixels to train a classi- fier.
Multi-modal Tracking for Object based SLAM
We present an on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from visual odometry.
Predicting Daily Activities From Egocentric Images Using Deep Learning
We collected a dataset of 40, 103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities.
Occlusion-Aware Object Localization, Segmentation and Pose Estimation
We present a learning approach for localization and segmentation of objects in an image in a manner that is robust to partial occlusion.
