In this paper, we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stops in the extreme case of exteroceptive sensors failure. The key components of the method are the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter. The method is tested on the KITTI odometry dataset, and our dead-reckoning inertial method based only on the IMU accurately estimates 3D position, velocity, orientation of the vehicle and self-calibrates the IMU biases. We achieve on average a 1.10% translational error and the algorithm competes with top-ranked methods which, by contrast, use LiDAR or stereo vision.

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AI-IMU Dead-Reckoning

The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.

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A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

In this article, we present self-calibrated visual–inertial odometry (VIO) that estimates inertial measurement unit (IMU) intrinsic parameters (scale factor and misalignment) using a stereo camera without any calibration boards. Most of the visual–inertial navigation algorithms assume that the visual and inertial sensors are well-calibrated before operating. However, this could degrade the navigation performance due to unmodeled errors. Specifically, we employ an extended Kalman filter (EKF)-based pose estimator in which the filter state is augmented by the IMU intrinsic parameter to model the egomotion more precisely. Since raw IMU readings are transformed by the intrinsic parameter, these are key factors that determine the performance of the egomotion tracking. The main contribution of this article is an analytic observability analysis of the self-calibrated VIO that is a nonlinear time-varying system. We inspect the rank of the observability matrix formed by Lie derivatives of the nonlinear system. Our theoretical result reveals that the IMU intrinsic parameter is fully observable when all six axes of an IMU are excited. This is further confirmed by our simulation experiments by examining state uncertainties. Moreover, the real-world experiment using a publicly available and author-collected data set reveals that the pose tracking performance is improved by modeling IMU intrinsic parameters.

Observability Analysis of IMU Intrinsic Parameters in Stereo Visual–Inertial Odometry

The introduction of autonomous vehicles can potentially lead to enhanced situational awareness and safety for road transport. However, the performance required for autonomous operations places stringent requirements on the vehicle navigation systems design. Relevant performance measures include not only the accuracy of the system but also its ability to detect sensor faults within a specified Time-to-Alert (TTA) and without generating an excessive number of false alarms. A new integrated navigation system architecture is proposed which utilizes Global Navigation Satellite Systems (GNSS), low-cost Inertial Navigation Systems (INS), visual odometry and Vehicle Dynamic Models (VDM). The system design is based on various navigation modes, each with independent failure mechanisms and fault-detection capabilities. A two-step data fusion approach is adopted to optimize the system accuracy and integrity performance. This includes a Knowledge-Based Module (KBM) performing a detailed sensor integrity analysis followed by a conventional Extended Kalman Filer (EKF). CAV navigation integrity requirement (i.e., alert limits and time-to-alert) are considered in the KBM where fault detection probabilities are calculated for each mode and translated to protection levels. A simulation case study is executed to verify the performance of each navigation mode in the presence of faults affecting the individual navigation sensors. Results confirm that the required CAV integrity performances are met, while the inclusion of visual odometry and VDM data provides significant performance enhancements both in terms of accuracy and integrity over existing INS/GNSS systems.

A High-Integrity and Low-Cost Navigation System for Autonomous Vehicles

Autonomous underwater vehicles (AUVs) play an important role in subaqueous construction and oceanographic survey, in which AUV self-localization is a crucial component. Current localization methods mainly depend on expensive acoustic positioning systems, which limit the wide application of AUVs. In this paper, referring to the low-cost visual localization method and focusing on challenges caused by underwater environment, we propose an underwater self-localization method based on Pseudo-3D vision-inertia for AUVs. The proposed method merges depth information into 2D visual image to achieve continuous and robust localization under dramatically changing underwater environment. In order to decrease errors, we propose an online fusion method based on tightly-coupled nonlinear optimization to fuse the measurements of the pre-integrated inertial measurement unit and the observations from the down-looking camera. We also optimize four degrees-of-freedom pose graph to enhance the global consistency and design an online loop detection module to realize the underwater relocalization. In addition, we develop a low-cost, portable, and small volume sensor suite for underwater vehicle localization and test the proposed self-localization method. We test the proposed method in the underwater environment using the custom-made sensor suite, and the experimental results demonstrate the effectiveness of the proposed method under dramatically changing underwater environment.

Pseudo-3D Vision-Inertia Based Underwater Self-Localization for AUVs

In this paper, we present a hybrid visual inertial navigation algorithm for an autonomous and intelligent vehicle that combines the multi-state constraint Kalman filter (MSCKF) with the nonlinear visual-inertial graph optimization. The MSCKF is a well-known visual inertial odometry (VIO) method that performs the fusion between an inertial measurement unit (IMU) and the image measurements within a sliding window. The MSCKF computes the re-projection errors from the camera measurements and the states in the sliding window. During this process, the structure-only estimation is performed without exploiting the full information over the window, like the relative interstate motion constraints and their uncertainties. The key contribution of this paper is combination of the filtering and non-linear optimization method for VIO, and the design of a novel measurement model that exploits all of the measurements and information available within the sliding window. The local visual-inertial optimization is performed using pre-integrated IMU measurements and camera measurements. It infers the probabilistically optimal relative pose constraints. These local optimal constraints are used to estimate the global states under the MSCKF framework. The proposed local-optimal-multi-state constraint Kalman filter is validated using a simulation data set, as well as publicly available real-world data sets generated from real-world urban driving experiments.

EKF-Based Visual Inertial Navigation Using Sliding Window Nonlinear Optimization

Lunar rover plays a key role in lunar exploration based on its maneuverability. For the successful operation of the lunar rover, a high-accuracy navigation technique has to be obtained. In order to perform high-accuracy inertial navigation, the accuracy of initial alignment is important. In general, initial alignment can be divided into two processes, coarse and fine alignment. In the general coarse alignment process, the acceleration and rotation rate of the stationary rover are used as reference vectors. However, the acceleration and rotation rate of the Moon are 1/6 and 1/27 times smaller than those of the Earth, respectively. As a result, even though the same sensors are used, the performance of initial coarse alignment gets worse on the Moon. In this paper, three coarse alignment methods using accelerometers and sun sensor, instead of accelerometers and gyros, are proposed and the associated errors are analyzed. For comparison, the existing general coarse alignment algorithms are summarized. The analyses are verified by appropriate computer simulations, and it shows that the proposed algorithms greatly improve the performance of coarse alignment, outperforming the existing algorithms at least on the Moon. Therefore, the proposed algorithm is suitable for lunar rover application, and can also be applied to other planetary explorations.

Sejong Heo

Papers

EKF-Based Visual Inertial Navigation Using Sliding Window Nonlinear Optimization

Coarse alignment of lunar exploration rover using accelerometer and sun sensor