Abstract Because of its high-precision, low-cost and easy-operation, Precise Point Positioning (PPP) becomes a potential attractive positioning technique that can be applied to self-driving cars drones. However, the reliability availability PPP will significantly degraded in extremely difficult conditions where Global Navigation Satellite System (GNSS) signals are blocked frequently. Inertial (INS) has been integrated with GNSS ameliorate such situations last decades. Recently,...

Abstract Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, global solution, has been widely applied due to its convenient operation. Nevertheless, performance PPP is severely affected by signal interference, especially GNSS-challenged environments. Inertial System (INS) aided GNSS can...

As a relative positioning technique, light detection and ranging (LiDAR)-inertial odometry (LIO) is known to suffer from drifting can only provide local coordinates. To compensate for these shortages of LIO, an effective way integrate global navigation satellite system (GNSS) with LIO. In this contribution, we proposed tightly coupled GNSS real-time kinematic (RTK)/inertial (INS)/LiDAR under the factor graph optimization framework, termed FGO-GIL, achieve high-precision continuous in urban...

Accurate, continuous and seamless state estimation is the fundamental module for intelligent navigation applications, such as self-driving cars autonomous robots. However, it often difficult a standalone sensor to fulfill demanding requirements of precise in complex scenarios. To fill this gap, letter proposes exploit complementariness global satellite system (GNSS), inertial measurement unit (IMU) vision via tightly coupled integration method, aiming achieve accurate urban environments....

Nowadays, portable cameras and LiDARs are widely applied in robotic applications for environment perception, path planning, high precision navigation. In such cases, accurate inter-sensor spatial transformation is critical to fuse these sensors seamlessly. However, the feasible efficient extrinsic calibration of LiDAR-camera sensor pair still challenging as it hard establish common feature correspondence between sparse LiDAR point clouds monocular images. this contribution, we design a novel...

The visual-inertial navigation system (VINS) is a low-cost sensor suite that utilizes inertial measurement unit (IMU) and camera data for vehicle ego-motion estimation environmental perception. For VINS, the accurate calibration of inter-sensor (IMU-camera, camera-camera) extrinsic parameters prerequisite optimal information fusion. Currently, modern vehicles are usually equipped with global satellite (GNSS) devices, which provide driftless position could be utilized to enhance calibration....

Continuous and reliable high-precision positioning in a wide area is an important task for autonomous driving. In this paper, we present GIVE, nonlinear optimization-based GNSS-inertial-visual (GIV) state estimator that tightly fuses multi-GNSS raw carrier phase pseudorange observations with inertial visual information to address the problem of continuous centimeter-level urban canyons where GNSS signal may be intermittent or even outage. The primary contribution work investigation terms...

Accurate position, velocity and orientation are essential for the autonomous navigation of unmanned vehicles. The integration GNSS INS that can deliver continuous states is widely used intelligent vehicle systems. In this paper, we propose a tightly coupled GNSS/INS positioning framework with carrier-phase ambiguity resolution based on factor graph optimization (FGO). approach, sliding window optimizer employed to fuse multi-GNSS pseudorange observations inertial measurements. same within...

High-definition (HD) map is crucial for intelligent vehicles to perform high-level localization and navigation. To improve the availability usability of HD map, it meaningful investigate crowd-sourced mapping solutions low-cost map-aided schemes which don't rely on high-end sensors. In this letter, we propose a novel vision-based system, could generate compact instance-level road maps automatically provide high-availability localization. The spatial uncertainties elements are taken into...

Nowadays, with the rapid development of mobile robotics and self-driving technology, there is a growing demand for high-accuracy, dependable seamless navigation. However, it unrealistic single-sensor-based technique to meet requirements practical applications in increasingly complex environments. To address this problem, we propose an optimization-based tightly-coupled precise point positioning (PPP)/inertial navigation system (INS)/Vision integration method achieve continuous state...

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it encounters challenges in vehicular scenarios, as the restricted motion of ground vehicle could lead degraded observability, and lack stable features might occur dynamic road environments. In this paper, we propose Ground-VIO, which utilizes specific camera-ground geometry enhance monocular VIO performance realistic method, modeled with vehicle-centered...

Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness applicability. However, there is a lack of research in fusing these learning-based multi-sensor information, which could be indispensable to push related applications large-scale complex scenarios. In this paper, we tightly integrate the trainable dense bundle adjustment (DBA) information through factor graph. framework, recurrent...

Accurate and real-time tracking of multiple moving objects in 3-D space is critical for intelligent transportation applications such as autonomous driving traffic monitoring. However, performance often hindered by difficulties occlusion or distant objects. To this end, we present a novel 3-D-light detection ranging (LiDAR) multiobject (MOT) system based on factor graph optimization (FGO) that simultaneously tracks reconstructs their models. First, develop object dynamic motion model combines...

Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness applicability. However, there is a lack of research in fusing these learning-based multi-sensor information, which could be indispensable to push related applications large-scale complex scenarios. In this paper, we tightly integrate the trainable dense bundle adjustment (DBA) information through factor graph. framework, recurrent...

For high-level geo-spatial applications and intelligent robotics, accurate global pose information is of crucial importance. Map-aided localization an important universal approach to overcome the limitations navigation satellite system (GNSS) in challenging environments. However, current solutions face challenges terms mapping flexibility, storage burden re-localization performance. In this work, we present SF-Loc, a lightweight visual map-aided system, whose core idea map representation...

Visual–inertial systems suffer from the issue of error accumulation, which limits their application to small-scale and short-duration scenarios. To enhance applicability visual–inertial odometry (VIO), we propose T-VIO, a filter-based estimator that tightly integrates visual, inertial, time-differenced carrier phase (TDCP) measurements for low-drift high-precision local pose estimation. The introduction TDCP suppresses accumulation provides absolute heading observability. On this basis,...

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it has been meeting challenges in vehicular scenarios due limited dynamics and lack of stable features. In this paper, we propose Ground-VIO, which utilizes ground features the specific camera-ground geometry enhance monocular VIO performance realistic road environments. method, modeled with vehicle-centered parameters integrated into an optimization-based...