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Found 10 papers, 2 papers with code
Robot Goes Fishing: Rapid, High-Resolution Biological Hotspot Mapping in Coral Reefs with Vision-Guided Autonomous Underwater Vehicles
Coral reefs are fast-changing and complex ecosystems that are crucial to monitor and study.
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods
In our experiments, we show that DeepSeeColor offers comparable performance to the popular "Sea-Thru" algorithm (Akkaynak & Treibitz, 2019) while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.
CUREE: A Curious Underwater Robot for Ecosystem Exploration
The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys.
Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehicles
To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp. whoi. edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations
We propose a generative model for the spatio-temporal distribution of high dimensional categorical observations.
Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments
We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator.
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate.
Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling
This paper explores the use of a Bayesian non-parametric topic modeling technique for the purpose of anomaly detection in video data.
Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring
This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility.
Gibbs Sampling Strategies for Semantic Perception of Streaming Video Data
Topic modeling of streaming sensor data can be used for high level perception of the environment by a mobile robot.
