An official website of the United States government
Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles. To enable collaborative localization, several model-based state estimation and learning-based localization methods have been developed. Given their encouraging performance, model-based state estimation often lacks the ability to model the complex relationships among multiple objects, while learning-based methods are typically not able to fuse the observations from an arbitrary number of views and cannot well model uncertainty. In this paper, we introduce a novel spatiotemporal graph filter approach that integrates graph learning and model-based estimation to perform multi-view sensor fusion for collaborative object localization. Our approach models complex object relationships using a new spatiotemporal graph representation and fuses multi-view observations in a Bayesian fashion to improve location estimation under uncertainty. We evaluate our approach in the applications of connected autonomous driving and multiple pedestrian localization. Experimental results show that our approach outperforms previous techniques and achieves the state-of-the-art performance on collaborative localization.
more »
« less
Localization in Seemingly Sensory-Denied Environments through Spatio-Temporal Varying Fields
Localization in underwater environments is a fundamental problem for autonomous vehicles with important applications such as underwater ecology monitoring, infrastructure maintenance, and conservation of marine species. However, several traditional sensing modalities used for localization in outdoor robotics (e.g., GPS, compasses, LIDAR, and Vision) are compromised in underwater scenarios. In addition, other problems such as aliasing, drifting, and dynamic changes in the environment also affect state estimation in aquatic environments. Motivated by these issues, we propose novel state estimation algorithms for underwater vehicles that can read noisy sensor observations in spatio-temporal varying fields in water (e.g., temperature, pH, chlorophyll-A, and dissolved oxygen) and have access to a model of the evolution of the fields as a set of partial differential equations. We frame the underwater robot localization in an optimization framework and formulate, study, and solve the state-estimation problem. First, we find the most likely position given a sequence of observations, and we prove upper and lower bounds for the estimation error given information about the error and the fields. Our methodology can find the actual location within a 95% confidence interval around the median in over 90% of the cases in different conditions and extensions.
more »
« less
- Award ID(s):
- 2024733
- PAR ID:
- 10471226
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2022 Sixth IEEE International Conference on Robotic Computing (IRC)
- ISBN:
- 978-1-6654-7260-9
- Page Range / eLocation ID:
- 142 to 147
- Format(s):
- Medium: X
- Location:
- Italy
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Scientists continue to study the red tide and fish-kill events happening in Florida. Machine learning applications using remote sensing data on coastal waters to monitor water quality parameters and detect harmful algal blooms are also being studied. Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) are often deployed on data collection and disaster response missions. To enhance study and mitigation efforts, robots must be able to use available data to navigate these underwater environments. In this study, we compute a satellite-derived underwater environment (SDUE) model by implementing a supervised machine learning model where remote sensing reflectance (Rrs) indices are labeled with in-situ data they correlate with. The models predict bathymetry and water quality parameters given a recent remote sensing image. In our experiment, we use Sentine1-2 (S2) images and in-situ data of the Biscayne Bay to create an SDUE that can be used as a Chlorophyll-a map. The SDUE is then used in an Extended Kalman Filter (EKF) application that solves an underwater vehicle localization and navigation problem.more » « less
-
Bearing only cooperative localization has been used successfully on aerial and ground vehicles. In this paper we present an extension of the approach to the underwater domain. The focus is on adapting the technique to handle the challenging visibility conditions underwater. Furthermore, data from inertial, magnetic, and depth sensors are utilized to improve the robustness of the estimation. In addition to robotic applications, the presented technique can be used for cave mapping and for marine archeology surveying, both by human divers. Experimental results from different environments, including a fresh water, low visibility, lake in South Carolina; a cavern in Florida; and coral reefs in Barbados during the day and during the night, validate the robustness and the accuracy of the proposed approach.more » « less
-
Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.more » « less
-
Unmanned Underwater Vehicles (UUVs) have a promising future to explore the polar regions. In this paper, we present our progress on developing a self-contain inertial odometry for under-ice navigation. Firstly, a microcontroller-based hardware time synchronization for multiple devices is demonstrated. Moreover, we present a new IMU, Doppler Velocity Log (DVL) and Pressure dead-reckoning (DR) for state estimation and a robust initialization approach for underwater vehciels. Field trials have been conducted in Utqiagvik, Alaska in March 2022 to gather multi-sensor data under the sea ice. In this paper, we highlight the performance of our method by comparing to the robot_localization algorithm, a widely used open-source localization algorithm.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IRC55401.2022.00032
