Finally, the semantic map is compressed and distributed to production cars, which use this map for localization.
In this paper, we exploit robust semantic features to build the map and localize vehicles in parking lots.
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 novel correction algorithm is proposed for multi-class classification problems with corrupted training data.
We validate the performance of our system on public datasets and through real-world experiments 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.
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.
We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios.
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.
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.
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.