Summary: Estimating the robot state within a known map is an essential problem for mobile robot; it is also referred to “localization”. Even LiDAR-based localization is practical in many applications, it is difficult to achieve global localization with LiDAR only for its low-dimension feedback, especially in environments with repetitive geometric features. A sensor-fusion-based localization system is introduced in this paper, which has the capability of addressing the global localization problem. Both LiDAR and vision sensors are integrated, making use of the rich information introduced by vision sensor and the robustness from LiDAR. A hybrid grid-map is built for global localization, and a visual global descriptor is applied to speed up the localization convergence, combined with a pose refining pipeline for improving the localization accuracy. Also, a trigger mechanism is introduced to solve kidnapped problem and verify the relocalization result. The experiments under different conditions are designed to evaluate the performance of the proposed approach, as well as a comparison with the existing localization systems. According to the experimental results, our system is able to solve the global localization problem, and the sensor-fusion mechanism in our system has an improved performance.

Robustness in visual odometry (VO) systems is critical, as it determines reliable performance in various scenarios and challenging environments. Especially with the development of data-driven technology, such as deep learning, the combination of data-driven VO and traditional model-based VO has achieved accurate tracking performance. However, the existence of local optimums in the model-based cost function still limits the robustness. In this study, we introduce a novel framework with a particle filter (PF) in the optimization process, where the PF is constructed by deep neural network (DNN) prediction. We propose constructing the PF by motion prediction classification and its uncertainty based on the characteristic of on-road driving motion. At the same time, an interval DNN prediction strategy is introduced to improve the real-time performance. Experimental results show that our framework obtains better tracking accuracy and robustness than the existing works, while the time consumption is maintained.

This paper aims to study the structure of the singular manifold of a fragment of protein backbone using the method of robot kinematics. By modeling chemical bonds as rigid links, and torsional motions about chemical bonds as revolute joints, a fragment of protein backbone turns into a kinematic linkage. The singular manifold of the forward kinematic map of this linkage contains important information about the structure of its inverse kinematics map, and the results also apply to the study of the conformation space of protein loops. By splitting the forward kinematic map into an orientation and position map, we derive equations of the singular manifolds for both maps and analyze their geometric and combinatorial structures. We discuss the application of singular manifolds in several problems of computational biology, including minimal parametrization of protein conformation space and conformation space sampling of protein loops.

The existing train positioning technology of the train control system relies on the track circuit, which has high construction and operation and maintenance costs. the GNSS/INS system can effectively improve the train positioning accuracy, but the global satellite navigation technology has the problem of signal loss in the complex operation environment, which increases the cumulative error of the inertial measurement unit. To meet the demand of future train results, this paper proposes a combined GNSS/INS positioning scheme that introduces a virtual transponder to calibrate the cumulative error of inertial measurement units. The proposed combination is simulated and tested, and the results show that the combined positioning system converges significantly in the train speed error as well as the mileage error, and can provide continuous high accuracy positioning for trains.