Event-Based Visual/Inertial Odometry for UAV Indoor Navigation.

Indoor navigation is becoming increasingly essential for multiple applications. It is complex and challenging due to dynamic scenes, limited space, and, more importantly, the unavailability of global navigation satellite system (GNSS) signals. Recently, new sensors have emerged, namely event cameras, which show great potential for indoor navigation due to their high dynamic range and low latency.

UAV-Based Image and LiDAR Fusion for Pavement Crack Segmentation.

Pavement surface maintenance is pivotal for road safety. There exist a number of manual, time-consuming methods to examine pavement conditions and spot distresses. More recently, alternative pavement monitoring methods have been developed, which take advantage of unmanned aerial systems (UASs).

INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments.

Traditionally, navigation systems have relied solely on global navigation satellite system (GNSS)/inertial navigation system (INS) integration. When a temporal loss of GNSS signal lock is encountered, these systems would rely on INS, which can sustain short bursts of outages, albeit drift significantly in prolonged outages. In this study, an extended Kalman filter (EKF) is proposed to develop an integrated INS/LiDAR/Stereo simultaneous localization and mapping (SLAM) navigation system.

Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments.

This research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal outages. The system features the use of an integrated INS/monocular visual simultaneous localization and mapping (SLAM) navigation system that takes advantage of LiDAR depth measurements to correct the scale ambiguity that results from monocular visual odometry. The proposed system was tested using two datasets, namely, the KITTI and the Leddar PixSet, which cover a wide range of driving environments.

A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments.

Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization.

An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments.

Traditional navigation systems rely on GNSS/inertial navigation system (INS) integration, in which the INS can provide reliable positioning during short GNSS outages. However, if the GNSS outage persists for prolonged periods of time, the performance of the system will be solely dependent on the INS, which can lead to a significant drift over time. As a result, the need to integrate additional onboard sensors is essential.

Precise Point Positioning Using World's First Dual-Frequency GPS/GALILEO Smartphone.

The release of the world's first dual-frequency GPS/Galileo smartphone, Xiaomi mi 8, in 2018 provides an opportunity for high-precision positioning using ultra low-cost sensors. In this research, the GNSS precise point positioning (PPP) accuracy of the Xiaomi mi 8 smartphone is tested in post-processing and real-time modes. Raw dual-frequency observations are collected over two different time windows from both of the Xiaomi mi 8 smartphone and a Trimble R9 geodetic-quality GNSS receiver using a short baseline, due to the lack of a nearby reference station to the observation site.

Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects.

Precise Point Positioning Using Triple GNSS Constellations in Various Modes.

This paper introduces a new dual-frequency precise point positioning (PPP) model, which combines the observations from three different global navigation satellite system (GNSS) constellations, namely GPS, Galileo, and BeiDou. Combining measurements from different GNSS systems introduces additional biases, including inter-system bias and hardware delays, which require rigorous modelling. Our model is based on the un-differenced and between-satellite single-difference (BSSD) linear combinations.

Performance Analysis of Several GPS/Galileo Precise Point Positioning Models.

This paper examines the performance of several precise point positioning (PPP) models, which combine dual-frequency GPS/Galileo observations in the un-differenced and between-satellite single-difference (BSSD) modes. These include the traditional un-differenced model, the decoupled clock model, the semi-decoupled clock model, and the between-satellite single-difference model. We take advantage of the IGS-MGEX network products to correct for the satellite differential code biases and the orbital and satellite clock errors.

Integration of GPS precise point positioning and MEMS-based INS using unscented particle filter.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used.