Published:
Real‐world robots will face a wide variety of complex environments when performing navigation or exploration tasks, especially in situations where the robots have never seen before. Usually, robots need to establish local or global maps and then use path planning algorithms to determine their routes. However, in some environments [Read More] 
Published:
Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm based on sliding window tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration approach utilizing complete IMU [Read More] 
Published:
By demonstrating robustness in scenarios characterized by high-speed motion and extreme lighting changes, event cameras hold great potential for enhancing the perception reliability of autonomous driving systems. Since its novelty and data sparsity, the progress of event-based algorithms is hindered by the scarcity of high-quality labeled [Read More] 
Published:
This article presents a challenging new dataset for indoor localization research. We have recorded the whole internal structure of Fengtai Wanda Plaza which is an area of over 15,800 m2 with a Navvis M6 device. The dataset contains 679 RGB-D panoramas and 2,664 query images collected by three different smartphones. [Read More]
Published:
Robust visual localization in traffic scenes is a fundamental problem for self-driving vehicles. However, it is still challenging to achieve accurate localization performance because of drastic viewpoint and illumination changes. To address the issues, we design a novel monocular localization framework based on a [Read More]
Published:
Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge. More specifically, reprojection error, [Read More]
Published:
Odometer-aided visual-inertial SLAM systems typically have a good performance for navigation of wheeled platforms, while they usually suffer from degenerate cases before the first turning. In this paper, firstly we perform an observability analysis w.r.t. the extrinsic parameters before the first turning, which is a [Read More]
Published:
Visual localization is a key technology in the field of 3D robot vision. One of its major difficulties lies in how to deal with the challenges brought by the appearance changes of query images and database images caused by large time spans. Many methods focus on extracting more robust features from images to deal with [Read More]
Published:
In this paper, we present a deep learning-based approach to monocular visual odometry. We propose a LCGR(Local Convolution and Global RNN) module which utilizes several independent 3D convolution layers to filter noise from features extracted by FlowNet, as well as to model local information, and a Bi-ConvLSTM layer to [Read More]
Published:
Visual localization is a fundamental problem in computer vision and robotics. Recently, deep learning has shown to be effective for robust monocular localization. Most deep learning-based methods utilize convolution neural network (CNN) to regress global 6 degree-of-freedom (Dof) pose. However, these methods suffer from [Read More]
Published:
Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration approach [Read More]
