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Found 43 papers, 23 papers with code
Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision
Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes.
VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance Fields
Building upon Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Instant-NGP), VIRUS-NeRF incorporates depth measurements from ultrasonic and infrared sensors and utilizes them to update the occupancy grid used for ray marching.
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End
In this paper, we model perception failures as invisible obstacles and pits, and train a reinforcement learning (RL) based local navigation policy to guide our legged robot.
Local and Global Information in Obstacle Detection on Railway Tracks
We evaluate our method on a custom dataset featuring railway images with artificially augmented obstacles.
Fast Traversability Estimation for Wild Visual Navigation
We demonstrate the advantages of our approach with experiments and ablation studies in challenging environments in forests, parks, and grasslands.
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.
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene change: changes in the position of an object, its semantic state, or the composition of a scene as a whole.
SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning
We further present an informative path planning method, leveraging the capabilities of our mapping approach and a novel scene-completion-aware information gain.
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.
Sampling-free obstacle gradients and reactive planning in Neural Radiance Fields (NeRF)
This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning.
Descriptellation: Deep Learned Constellation Descriptors
However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction.
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.
Superquadric Object Representation for Optimization-based Semantic SLAM
Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant view-point and appearance changes.
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.
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.
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.
Out-of-Distribution Detection for Automotive Perception
A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode.
Freetures: Localization in Signed Distance Function Maps
Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps.
Robotics
Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM
The first challenge is addressed by the use of a convolutional network that learns a multi-class semantic segmentation of the image.
Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude.
Learning Camera Miscalibration Detection
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.
3D Gated Recurrent Fusion for Semantic Scene Completion
This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion.
Ranked #14 on
3D Semantic Scene Completion
on NYUv2
Depth Based Semantic Scene Completion with Position Importance Aware Loss
We propose PALNet, a novel hybrid network for SSC based on single depth.
VersaVIS: An Open Versatile Multi-Camera Visual-Inertial Sensor Suite
Robust and accurate pose estimation is crucial for many applications in mobile robotics.
Robotics
SegMap: Segment-based mapping and localization using data-driven descriptors
We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds.
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.
3D Multi-Robot Patrolling with a Two-Level Coordination Strategy
This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts.
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)
OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios
We introduce a novel method for oriented place recognition with 3D LiDAR scans.
Robotics
Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery
To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes.
Robotics
VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments
Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments.
Robotics
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization.
Ranked #3 on
Visual Place Recognition
on Berlin Kudamm
Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames.
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating in large and changing environments.
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.
SegMap: 3D Segment Mapping using Data-Driven Descriptors
While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.
X-View: Graph-Based Semantic Multi-View Localization
Our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points.
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles.
Robotics
SegMatch: Segment based loop-closure for 3D point clouds
We propose SegMatch, a reliable loop-closure detection algorithm based on the matching of 3D segments.
Robotics
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it.
Robotics
