Search Results for author:
Found 44 papers, 26 papers with code
A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions.
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs
We provide an open-source codebase for training and deployment.
Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming
It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying.
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i. e., a semantic segmentation.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #32 on
3D Semantic Segmentation
on SemanticKITTI
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor.
Robotics
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Ranked #19 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching
We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric.
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.
Ranked #2 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
4D Panoptic LiDAR Segmentation
In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs).
Video Contrastive Learning with Global Context
Our formulation is able to capture global context in a video, thus robust to temporal content change.
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation
Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems.
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments
We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization.
SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems.
Robust Double-Encoder Network for RGB-D Panoptic Segmentation
We propose a novel encoder-decoder neural network that processes RGB and depth separately through two encoders.
IR-MCL: Implicit Representation-Based Online Global Localization
The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization.
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data.
Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation
The preliminary experiments suggest that the cost maps generated by our network are suitable for the MPC and can guide the agent to the semantic goal more efficiently than a baseline approach.
Fully On-board Low-Power Localization with Multizone Time-of-Flight Sensors on Nano-UAVs
Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31. 2m$\boldsymbol{^2}$ map with 0. 15m accuracy and an above 95% success rate.
Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data
Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning.
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving
Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping
Our framework combines the mapped acquisition function information into the UAV's planning objectives.
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Monitoring plants and fruits at high resolution play a key role in the future of agriculture.
Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization
We exploit 3D object detections from monocular RGB frames for both, the object-based map construction, and for globally localizing in the constructed map.
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics
Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
The production of food, feed, fiber, and fuel is a key task of agriculture.
PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics
In this work we present PAg-NeRF which is a novel NeRF-based system that enables 3D panoptic scene understanding.
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds
In this paper, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks.
Data-Centric Digital Agriculture: A Perspective
In response to the increasing global demand for food, feed, fiber, and fuel, digital agriculture is rapidly evolving to meet these demands while reducing environmental impact.
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
We propose a planning method for semi-supervised active learning of semantic segmentation that substantially reduces human labelling requirements compared to fully supervised approaches.
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment.
Unsupervised Pre-Training for 3D Leaf Instance Segmentation
Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping.
PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation.
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
To address these issues, we propose a novel deep reinforcement learning approach for adaptively replanning robot paths to map targets of interest in unknown 3D environments.
Open-World Semantic Segmentation Including Class Similarity
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles.
STAIR: Semantic-Targeted Active Implicit Reconstruction
The key components of our framework are a semantic implicit neural representation and a compatible planning utility function based on semantic rendering and uncertainty estimation, enabling adaptive view planning to target objects of interest.
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input, producing a scene with more details compared to state-of-the-art scene completion methods.
Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning
Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment.
