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Found 17 papers, 8 papers with code
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
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.
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.
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.
Online Temporal Calibration for Monocular Visual-Inertial Systems
Visual and inertial fusion is a popular technology for 6-DOF state estimation in recent years.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
