Search Results for author:
Found 16 papers, 7 papers with code
"Where am I?" Scene Retrieval with Language
As such, we need methods that interface between natural language and map representations of the environment.
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds
This allows for accurate detection directly in 3D scenes, object- and environment-aware grasp prediction, as well as robust and repeatable robotic manipulation.
LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire.
Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach
However, localizing e. g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes.
Unsupervised Continual Semantic Adaptation through Neural Rendering
Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance.
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
In order to operate in human environments, a robot's semantic perception has to overcome open-world challenges such as novel objects and domain gaps.
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection
The proposed framework infers task failures by evaluating the individual prediction, across multiple visual perception tasks for different regions in an image.
Self-Improving Semantic Perception for Indoor Localisation
We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments.
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes.
Pixel-wise Anomaly Detection in Complex Driving Scenes
The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving.
Ranked #3 on
Anomaly Detection
on Lost and Found
(using extra training data)
The Hidden Uncertainty in a Neural Networks Activations
We find that this leads to improved OOD detection of epistemic uncertainty at the cost of ambiguous calibration close to the data distribution.
A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction
We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites.
This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity
There has been a remarkable progress in the accuracy of semantic segmentation due to the capabilities of deep learning.
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes
Foreground objects are therefore detected as areas in an image for which the descriptors are unlikely given the background distribution.
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Deep learning has enabled impressive progress in the accuracy of semantic segmentation.
Ranked #13 on
Anomaly Detection
on Fishyscapes L&F
(using extra training data)
Modular Sensor Fusion for Semantic Segmentation
Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations.
