Online Domain Adaptation for Occupancy Mapping

@article{Tompkins2020OnlineDA,
  title={Online Domain Adaptation for Occupancy Mapping},
  author={Anthony Tompkins and Ransalu Senanayake and Fabio Tozeto Ramos},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.00164}
}
Creating accurate spatial representations that take into account uncertainty is critical for autonomous robots to safely navigate in unstructured environments. Although recent LIDAR based mapping techniques can produce robust occupancy maps, learning the parameters of such models demand considerable computational time, discouraging them from being used in real-time and large-scale applications such as autonomous driving. Recognizing the fact that real-world structures exhibit similar geometric… Expand
Object-Driven Active Mapping for More Accurate Object Pose Estimation and Robotic Grasping
TLDR
The first active object mapping framework for complex robotic grasping tasks built on an object SLAM system integrated with a simultaneous multi-object pose estimation process and an object-driven exploration strategy to guide the object mapping process is presented. Expand
Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional data
TLDR
A novel formulation of optimal transport problem in functional spaces is introduced and an efficient learning algorithm for finding the stochastic map between functional domains is developed and tested on real-world datasets of robot arm trajectories and digit numbers. Expand
Object SLAM-Based Active Mapping and Robotic Grasping
TLDR
The first active object mapping framework for complex robotic manipulation and autonomous perception tasks is presented, built on an object SLAM system integrated with a simultaneous multiobject pose estimation process that is optimized for robotic grasping. Expand

References

SHOWING 1-10 OF 45 REFERENCES
Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments
TLDR
This work proposes a method to propagate motion uncertainty into the kernel using a hierarchical model that can directly predict the occupancy state of the map in the future from past observations, being a valuable tool for robot trajectory planning under uncertainty. Expand
Learning highly dynamic environments with stochastic variational inference
TLDR
This work uses recent advancements in stochastic variational inference (SVI) to quickly learn dynamic areas in an online fashion and proposes an information-driven technique to “intelligently” select inducing points required for SVI without relying on any object trackers which essentially improves computational time as well as robustness. Expand
Bayesian Hilbert Maps for Dynamic Continuous Occupancy Mapping
TLDR
A variational Bayesian approach to Hilbert mapping, thus eliminating the regularization term typically adjusted heuristically and extended to learn long-term occupancy maps in dynamic environments in a sequential fashion, demonstrating the power of kernel methods to capture abstract nonlinear patterns and Bayesian learning to construct sophisticated models. Expand
Building Continuous Occupancy Maps With Moving Robots
TLDR
This work provides a theoretical analysis to compare and contrast the two major branches of Bayesian continuous occupancy mapping techniques— Gaussian process occupancy maps and Bayesian Hilbert maps—considering the fact that both utilize kernel functions to operate in a rich high-dimensional implicit feature space and use variational inference to learn parameters. Expand
Gaussian process occupancy maps*
TLDR
The technique can handle noisy data, potentially from multiple sources, and fuse it into a robust common probabilistic representation of the robot’s surroundings, and provides inferences with associated variances into occluded regions and between sensor beams, even with relatively few observations. Expand
Hilbert maps: Scalable continuous occupancy mapping with stochastic gradient descent
TLDR
A new technique for environment representation through continuous occupancy mapping that improves on the popular occupancy grip maps, and allows for efficient stochastic gradient optimization where each measurement is only processed once during learning in an online manner is devised. Expand
Automorphing Kernels for Nonstationarity in Mapping Unstructured Environments
TLDR
Experiments conducted on simulated and real-world datasets in static and dynamic environments indicate the proposed method significantly outperforms existing stationary occupancy mapping techniques, verifying the importance of learning the interdependent position-shape relationship of kernels alongside other model parameters. Expand
Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion
TLDR
A novel algorithm to produce descriptive online 3D occupancy maps using Gaussian processes, which may serve both as an improved-accuracy classifier, and as a predictive tool to support autonomous navigation. Expand
H-SLAM: Rao-Blackwellized Particle Filter SLAM Using Hilbert Maps
TLDR
A SLAM (Simultaneous Localization and Mapping) framework capable of obtaining this representation online is presented and it is demonstrated that this approach is able to represent the environment more consistently while capable of running online. Expand
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
TLDR
This work study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images, including a novel extension of pixel-level domain adaptation that is term the GraspGAN. Expand
...
1
2
3
4
5
...