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Found 12 papers, 6 papers with code
Open-world Semantic Segmentation for LIDAR Point Clouds
Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e. g., autonomous driving, since it is closed-set and static.
A Thin Format Vision-Based Tactile Sensor with A Micro Lens Array (MLA)
Vision-based tactile sensors have been widely studied in the robotics field for high spatial resolution and compatibility with machine learning algorithms.
Open-set 3D Object Detection
The first step is solved by the finding that unknown objects are often classified as known objects with low confidence, and we show that the Euclidean distance sum based on metric learning is a better confidence score than the naive softmax probability to differentiate unknown objects from known objects.
Deep Metric Learning for Open World Semantic Segmentation
Incrementally learning these OOD objects with few annotations is an ideal way to enlarge the knowledge base of the deep learning models.
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation.
Stereo Matching by Self-supervision of Multiscopic Vision
Self-supervised learning for depth estimation possesses several advantages over supervised learning.
Learning to Predict Vehicle Trajectories with Model-based Planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving.
Self-supervised Object Tracking with Cycle-consistent Siamese Networks
Self-supervised learning for visual object tracking possesses valuable advantages compared to supervised learning, such as the non-necessity of laborious human annotations and online training.
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.
Active Perception with A Monocular Camera for Multiscopic Vision
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation for robotic applications.
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors
In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects.
FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network
Tactile sensing is essential to the human perception system, so as to robot.
