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1. Neural-Kalman GNSS/INS Navigation for Precision Agriculture

   Du, Y. ( January 2023 , 2023 IEEE International Conference on Robotics and Automation)

   Precision agricultural robots require highresolution navigation solutions. In this paper, we introduce a robust neural-inertial sequence learning approach to track such robots with ultra-intermittent GNSS updates. First, we propose an ultra-lightweight neural-Kalman filter that can track agricultural robots within 1.4 m (1.4 - 5.8× better than competing techniques), while tracking within 2.75 m with 20 mins of GPS outage. Second, we introduce a user-friendly video-processing toolbox to generate high-resolution (±5 cm) position data for fine-tuning pre-trained neural-inertial models in the field. Third, we introduce the first and largest (6.5 hours, 4.5 km, 3 phases) public neural-inertial navigation dataset for precision agricultural robots. The dataset, toolbox, and code are available at: https://github.com/nesl/agrobot. 

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2. Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges

   https://doi.org/10.1109/PLANS53410.2023.10139997  

   Saha, Swapnil Sayan ; Du, Yayun ; Sandha, Sandeep Singh ; Garcia, Luis Antonio ; Jawed, Mohammad Khalid ; Srivastava, Mani ( April 2023 , 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS))

   Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31–134 x smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward. 

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3. TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

   https://doi.org/10.1145/3534594  

   Saha, Swapnil Sayan ; Sandha, Sandeep Singh ; Garcia, Luis Antonio ; Srivastava, Mani ( July 2022 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to preceding inertial perturbations. We also expand 2D sequence learning to 3D using a model-free barometric g-h filter robust to inertial and environmental variations. We evaluate TinyOdom for a wide spectrum of inertial odometry applications and target hardware against competing methods. Specifically, we consider four applications: pedestrian, animal, aerial, and underwater vehicle dead-reckoning. Across different applications, TinyOdom reduces the size of neural inertial models by 31× to 134× with 2.5m to 12m error in 60 seconds, enabling the direct deployment of models on URC devices while still maintaining or exceeding the localization resolution over the state-of-the-art. The proposed barometric filter tracks altitude within ±0.1m and is robust to inertial disturbances and ambient dynamics. Finally, our ablation study shows that the introduced magnetometer, physics, and velocity-centric sequence learning formulation significantly improve localization performance even with notably lightweight models. 

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4. Processed bathymetric data (netCDF grid) from 9°50'N, East Pacific Rise, acquired in 2018, 2019 and 2021 during AUV Sentry near-bottom dives

   https://doi.org/10.26022/IEDA/330373  

   Parnell-Turner, Ross ; Fornari, Daniel ; McDermott, Jill ; Barreyre, Thibaut ; Wu, Jyun-Nai ( January 2022 , Interdisciplinary Earth Data Alliance (IEDA))

   Bathymetric data were collected during a total of 19 AUV Sentry dives conducted from R/V Atlantis and R/V Roger Revelle during 2018 Atlantis cruise AT42-06 (two dives); 2019 Atlantis cruise AT42-21 (ten dives); and, 2021 Revelle cruise RR2102 (seven dives). Track lines were spaced 170 m apart, with Sentry about 65 m above bottom, collecting multibeam data using a 400 kHz Reson 7125 system in 2018, and a 400 kHz Kongsberg EM2040 system in 2019 and 2021. Sentry navigation was obtained using a 300 kHz Teledyne Doppler velocity log (DVL) and a Sonardyne AvTrak2 ultra-short baseline (USBL) acoustic positioning system, combined with an iXblue Phins inertial navigation system (INS), and a Paroscientific 8B7000-I Digiquartz depth sensor. Sonar data from all 19 dives were processed together using the open-source MB-System software, and were gridded at 1 m × 1 m node resolution using a beam footprint calculated with the angular beam widths and weighted by local slope. The grid file is in GMT-compatible netCDF format, in un-projected geographic coordinates. Funding was provided by National Science Foundation awards OCE-1834797, OCE1949485, OCE-1948936, and OCE-1949938. 

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5. Position Control and Variable-Height Trajectory Tracking of a Soft Pneumatic Legged Robot

   https://doi.org/10.1109/IROS51168.2021.9635966  

   Liu, Zhichao ; Karydis, Konstantinos ( September 2021 , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Soft pneumatic legged robots show promise in their ability to traverse a range of different types of terrain, including natural unstructured terrain met in applications like precision agriculture. They can adapt their body morphology to the intricacies of the terrain at hand, thus enabling robust and resilient locomotion. In this paper we capitalize upon recent developments on soft pneumatic legged robots to introduce a closed-loop trajectory tracking control scheme for operation over flat ground. Closed-loop pneumatic actuation feedback is achieved via a compact and portable pneumatic regulation board. Experimental results reveal that our soft legged robot can precisely control its body height and orientation while in quasi-static operation based on a geometric model. The robot can track both straight line and curved trajectories as well as variable-height trajectories. This work lays the basis to enable autonomous navigation for soft legged robots. 

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