Authors: Zeev Yampolsky, Felipe O. Silva, Adriano Frutuoso, Itzik Klein
Organizations: The Hatter Department of Marine Technologies, Charney School of Marine Sciences, University of Haifa, Haifa, Israel · Department of Automatics, School of Engineering Federal University of Lavras, Lavras, MG, 37200-900, Brazil · Department of Higher Education Federal Institute of Amazonas, Manaus, AM, 69075-351, Brazil
Autonomous platforms operating in the oceans require accurate navigation to successfully complete their mission. In this regard, the initial heading estimation accuracy and the time required to achieve it play a critical role. The initial heading is traditionally estimated by model-based approaches employing orientation decomposition. However, methods such as the dual vector decomposition and optimized attitude decomposition achieve satisfactory heading accuracy only after long alignment times. To allow rapid and accurate initial heading estimation, we propose an end-to-end, model-free, neural-assisted framework using the same inputs as the model-based approaches. Our proposed approach was trained and evaluated on real-world dataset captured by an autonomous surface vehicle. Our approach shows a significant accuracy improvement over the model-based approaches achieving an average absolute error improvement of 53%. Additionally, our proposed approach was able to reduce the alignment time by up to 67%. Thus, by employing our proposed approach, the reduction in alignment time and improved accuracy allow for a shorter deployment time of an autonomous platform and increased navigation accuracy during the mission.
Fig. 2 : Detailed block diagram showing the proposed neural-assisted in-motion self-heading alignment architecture, that is HeadingNet, and the possible variation due to TAlign variations.
Fig. 3 : Illustration of an average pooling operation with a kernel of size k , applied over a T second measurement window containing n IMU measurements and m transport-rate and gravity measurements, where n>m .
Fig. 4 : Data flow diagram of starting from the raw measurements from the ASV sensors, the IMU and GNSS-RTK, to our proposed approach HeadingNet.
Fig. 5 : Heading angles plot of the first 350 seconds for the five recorded trajectories, R1 to R5.
Fig. 6 : Heading angles plot of the four evaluation recordings, EvalR1 to EvalR4.
Fig. 7 : Block diagram illustrating the data preparation pipeline to generate the training sets and datasets and evaluation set to train and evaluation all HeadingNet variations.
Fig. 8 : AE Results of the heading estimation in degrees for the four evaluation recordings, EvalR1 to EvalR4, at the examined alignment time periods, for HeadingNet and the four baseline methods.
Fig. 9 : Average AE heading in degrees results of all baseline methods and HeadingNet variations. The black dashed line represents the heading AE of HeadingNet10, and the red dashed line represents the HeadingNet30 results, thus the proposed approach HeadingNet result at these two alignment times.
Fig. 10 : AE improvement bar plot in percentage showing our proposed the improvement of our proposed HeadingNet over the best baseline method.
Autonomous underwater vehicles (AUVs) rely on the fusion of inertial navigation systems (INS) and Doppler velocity logs (DVL) for accurate navigation. Before deployment, this fusion requires a DVL initialization pipeline consisting of two stages: alignment, which estimates the rotation between the INS and DVL frames, and calibration, which estimates the DVL error terms. Conventionally, both stages are solved with model-based algorithms that demand complex vehicle maneuvers, surface-level satellite reference measurements, and simplified error models, making initialization time-consuming, trajectory-dependent, and sensitive to sensor quality. In this work, we propose a fully neural- aided DVL initialization pipeline that replaces both stages with two complementary neural networks: ResAlignNet for alignment and DCNet for calibration. The unified pipeline operates in situ on a single nearly constant-velocity trajectory and uses the same inputs as the model-based baseline. Using real-world data recorded across five distinct sensor error-term combinations, the proposed pipeline reduces the velocity root mean squared error by an average of 68.7% over the model-based baseline, using only 25s of data for initialization.
Guy Damari, Zeev Yampolsky, Itzik Klein
The Hatter Department of Marine Technologies Charney School of Marine Sciences, University of Haifa Haifa, Israel
This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 degree panoramic image around the USV. We design a CNN architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model (DEM) data in the Fourier domain using a MOSSE correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in a field trial in the archipelago. Our approach provides promising results by achieving position estimates with GPS-level accuracy.
Bertil Grelsson, Andreas Robinson, Michael Felsberg +1
Computer Vision Laboratory, Department of Electrical Engineering, Linköping University, Sweden · Inception Institute of Artificial Intelligence, Abu Dhabi, UAE
This paper presents a novel initialization method for range-aided simultaneous localization and mapping (RA-SLAM). The general SLAM problem has a well-known separable structure where landmark and robot positions can be solved for in a linear fashion given known robot headings. This paper considers the case where highly accurate heading information is available, which is typical of autonomous underwater vehicle (AUV) navigation, to solve for the range transponder and robot positions. The proposed approach consists of two steps. First, using a generalized trust region subproblem (GTRS), the positions of the transponders relative to the AUV are solved for given the nonlinear range measurements. Second, the relative transponder positions and the known heading of the AUV are used to estimate the transponder and AUV positions by solving a linear least-squares problem. These transponder and AUV position estimates, combined with the highly accurate heading information, provide a reliable initialization method for the general nonlinear RA-SLAM problem. The effectiveness of the proposed approach is tested on a real-world AUV dataset where long baseline (LBL) range measurements are provided in concert with highly accurate heading information provided by an inertial navigation system (INS).
Isabel Lougheed, James Richard Forbes
Department of Mechanical Engineering McGill University Montreal, QC, Canada