Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments.

In this paper, we introduce a real-time parallel-serial algorithm for autonomous robot positioning for GPS-denied, dark environments, such as caves and mine galleries. To achieve a good complexity-accuracy trade-off, we fuse data from light detection and ranging (LiDAR) and an inertial measurement unit (IMU). The proposed algorithm's main novelty is that, unlike in most algorithms, we apply an extended Kalman filter (EKF) to each LiDAR scan point and calculate the location relative to a triangular mesh.

[Possibilities of data acquisition, recording and processing based on the system for continuous intracranial pressure and cerebral perfusion pressure monitoring designed at the Department of Neurosurgery of the Jagiellonian University in Cracow].

Objectives: To describe computer system for intracranial pressure (ICP) monitoring and for infusion testing (IT), and to present current possibilities of analysis of cerebrospinal fluid (CSF) pressure signal.

System Description: Computer system for pressure signal measurement and acquisition consists of three sensors: for ICP, infusion pressure during IT, and blood pressure measurements. Electric signal from the sensors is transmitted to Temed RMN-201M cardiomonitor, and next after enhancement is subjected to Data Shuttle DS-12-8-5B-AO analog to digital converter.