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Found 22 papers, 6 papers with code
LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and Mapping
Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy.
NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects
We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes.
Optimizing Fiducial Marker Placement for Improved Visual Localization
Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene.
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images.
Probabilistic Volumetric Fusion for Dense Monocular SLAM
We present a novel method to reconstruct 3D scenes from images by leveraging deep dense monocular SLAM and fast uncertainty propagation.
Robust Change Detection Based on Neural Descriptor Fields
The ability to reason about changes in the environment is crucial for robots operating over extended periods of time.
PlaneSDF-based Change Detection for Long-term Dense Mapping
Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach: (1) PlaneSDF volumes are instantiated within each scene and registered across scenes using plane poses; 2D height maps and object maps are extracted per volume via height projection and connected component analysis.
Discrete-Continuous Smoothing and Mapping
In particular, we provide a library, DC-SAM, extending existing tools for inference problems defined in terms of factor graphs to the setting of discrete-continuous models.
Trajectory Prediction with Linguistic Representations
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions.
TIP: Task-Informed Motion Prediction for Intelligent Vehicles
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples.
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction.
Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors.
Lidar-Monocular Surface Reconstruction Using Line Segments
We compare the accuracy and the completeness of the 3D mesh to a ground truth obtained with a survey-grade 3D scanner.
A Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior
Recent achievements in depth prediction from a single RGB image have powered the new research area of combining convolutional neural networks (CNNs) with classical simultaneous localization and mapping (SLAM) algorithms.
Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation
For a robot deployed in the world, it is desirable to have the ability of autonomous learning to improve its initial pre-set knowledge.
Advances in Inference and Representation for Simultaneous Localization and Mapping
Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supporting such core functions as planning, navigation, and control.
CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy
Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems.
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems.
Towards Visual Ego-motion Learning in Robots
Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed.
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
High-Performance and Tunable Stereo Reconstruction
Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance.
A Mixture of Manhattan Frames: Beyond the Manhattan World
Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system.
