A single sensor system for mapping in GNSS-denied environments

An Integrated LSTM-Rule-Based Fusion Method for the Localization of Intelligent Vehicles in a Complex Environment

To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localization deviation and confidence factor to improve the initial fusion localization accuracy. Then, an odometer model for obtaining the projected localization trajectory is constructed. Considering the high accuracy of the odometer's projected trajectory within a short distance, we used the shape of the projected localization trajectory to inhibit the initial fusion localization noise and used trajectory matching to obtain an accurate localization. Finally, the Dual-LSTM network is constructed to predict the localization and build an electronic fence to guarantee the safety of the vehicle while also guaranteeing the updating of short-distance localization information of the vehicle when the above-mentioned fusion localization is unreliable. Under the limited arithmetic condition of the vehicle platform, accurate and reliable localization is realized in a complex environment. The proposed method was verified by long-time operation on the real vehicle platform, and compared with the EKF fusion localization method, the average root mean square error of localization was reduced by 66%, reaching centimeter-level localization accuracy.

A Reconfigurable Framework for Vehicle Localization in Urban Areas

Accurate localization for autonomous vehicle operations is essential in dense urban areas. In order to ensure safety, positioning algorithms should implement fault detection and fallback strategies. While many strategies stop the vehicle once a failure is detected, in this work a new framework is proposed that includes an improved reconfiguration module to evaluate the failure scenario and offer alternative positioning strategies, allowing continued driving in degraded mode until a critical failure is detected. Furthermore, as many failures in sensors can be temporary, such as GPS signal interruption, the proposed approach allows the return to a non-fault state while resetting the alternative algorithms used in the temporary failure scenario. The proposed localization framework is validated in a series of experiments carried out in a simulation environment. Results demonstrate proper localization for the driving task even in the presence of sensor failure, only stopping the vehicle when a fully degraded state is achieved. Moreover, reconfiguration strategies have proven to consistently reset the accumulated drift of the alternative positioning algorithms, improving the overall performance and bounding the mean error.

LiDAR/RISS/GNSS Dynamic Integration for Land Vehicle Robust Positioning in Challenging GNSS Environments

The autonomous vehicles (AV) industry has a growing demand for reliable, continuous, and accurate positioning information to ensure safe traffic and for other various applications. Global navigation satellite system (GNSS) receivers have been widely used for this purpose. However, GNSS positioning accuracy deteriorates drastically in challenging environments such as urban environments and downtown cores. Therefore, inertial sensors are widely deployed inside the land vehicle for various purposes, including the integration with GNSS receivers to provide positioning information that can bridge potential GNSS failures. However, in dense urban areas and downtown cores where GNSS receivers may incur prolonged outages, the integrated positioning solution may become prone to severe drift resulting in substantial position errors. Therefore, it is becoming necessary to include other sensors and systems that can be available in future land vehicles to be integrated with both the GNSS receivers and inertial sensors to enhance the positioning performance in such challenging environments. This work aims to design and examine the performance of a multi-sensor system that fuses the GNSS receiver data with not only the three-dimensional reduced inertial sensor system (3D-RISS), but also with the three-dimensional point cloud of onboard light detection and ranging (LiDAR) system. In this paper, a comprehensive LiDAR processing and odometry method is developed to provide a continuous and reliable positioning solution. In addition, a multi-sensor Extended Kalman filtering (EKF)-based fusion is developed to integrate the LiDAR positioning information with both GNSS and 3D-RISS and utilize the LiDAR updates to limit the drift in the positioning solution, even in challenging or ultimately denied GNSS environment. The performance of the proposed positioning solution is examined using several road test trajectories in both Kingston and Toronto downtown areas involving different vehicle dynamics and driving scenarios. The proposed solution provided a performance improvement over the standalone inertial solution by 64%. Over a GNSS outage of 10 min and 2 km distance traveled, our solution achieved position errors less than 2% of the distance travelled.