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Found 14 papers, 8 papers with code
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 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.
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
