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Found 24 papers, 9 papers with code
GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping
Our map system comprises a global Gaussian map and a sliding window of Gaussians, along with an IESKF-based odometry.
Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures
In this paper, we propose a cross-modal visual relocalization system in prior LiDAR maps utilizing intensity textures, which consists of three main modules: map projection, coarse retrieval, and fine relocalization.
Integrated Location Sensing and Communication for Ultra-Massive MIMO With Hybrid-Field Beam-Squint Effect
The advent of ultra-massive multiple-input-multiple output systems holds great promise for next-generation communications, yet their channels exhibit hybrid far- and near- field beam-squint (HFBS) effect.
ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning
Real-vehicle experiments further validate the feasibility and effectiveness of the method proposed in this paper.
MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps
Robust localization is the cornerstone of autonomous driving, especially in challenging urban environments where GPS signals suffer from multipath errors.
BLOS-BEV: Navigation Map Enhanced Lane Segmentation Network, Beyond Line of Sight
In this paper, we propose BLOS-BEV, a novel BEV segmentation model that incorporates SD maps for accurate beyond line-of-sight perception, up to 200m.
Crowd-Sourced NeRF: Collecting Data from Production Vehicles for 3D Street View Reconstruction
This approach solves the key problem of large-scale reconstruction, that is where the data comes from and how to use them.
PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment
To this end, we introduce PLGSLAM, a neural visual SLAM system capable of high-fidelity surface reconstruction and robust camera tracking in real-time.
FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow
However, how to cognize the ``road'' for automated vehicles where there is no well-defined ``roads'' remains an open problem.
Large-scale single-photon imaging
Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing.
RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving
Finally, the semantic map is compressed and distributed to production cars, which use this map for localization.
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot
In this paper, we exploit robust semantic features to build the map and localize vehicles in parking lots.
Data-driven learning of non-autonomous systems
To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances.
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data
A novel correction algorithm is proposed for multi-class classification problems with corrupted training data.
Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data
We present a numerical approach for approximating unknown Hamiltonian systems using observation data.
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization.
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
We validate the performance of our system on public datasets and through real-world experiments with multiple sensors.
Data Driven Governing Equations Approximation Using Deep Neural Networks
We demonstrate that the ResNet block can be considered as a one-step method that is exact in temporal integration.
Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion
By introducing an additional constraint in the time domain, our monocular visual-inertial tracking system can obtain continuous six degree of freedom (6-DoF) pose estimation without scale ambiguity.
Online Temporal Calibration for Monocular Visual-Inertial Systems
Visual and inertial fusion is a popular technology for 6-DOF state estimation in recent years.
Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios.
Reducing Parameter Space for Neural Network Training
For neural networks (NNs) with rectified linear unit (ReLU) or binary activation functions, we show that their training can be accomplished in a reduced parameter space.
Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM
In this paper, we propose a monocular visual-inertial SLAM system, which can relocalize camera and get the absolute pose in a previous-built map.
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
A monocular visual-inertial system (VINS), consisting of a camera and a low-cost inertial measurement unit (IMU), forms the minimum sensor suite for metric six degrees-of-freedom (DOF) state estimation.
Robotics
