An official website of the United States government
In this paper, we propose a novel approach for underwater simultaneous localization and mapping using a multibeam imaging sonar for 3D terrain mapping tasks. The high levels of noise and the absence of elevation angle information in sonar images present major challenges for data association and accurate 3D mapping. Instead of repeatedly projecting extracted features into Euclidean space, we apply optical flow within bearing-range images for tracking extracted features. To deal with degenerate cases, such as when tracking is interrupted by noise, we model the subsea terrain as a Gaussian Process random field on a Chow–Liu tree. Terrain factors are incorporated into the factor graph, aimed at smoothing the terrain elevation estimate. We demonstrate the performance of our proposed algorithm in a simulated environment, which shows that terrain factors effectively reduce estimation error. We also show ROV experiments performed in a variable-elevation tank environment, where we are able to construct a descriptive and smooth height estimate of the tank bottom.
more »
« less
Bayesian Generalized Kernel Inference for Terrain Traversability Mapping
We propose a new approach for traversability mapping with sparse lidar scans collected by ground vehicles, which leverages probabilistic inference to build descriptive terrain maps. Enabled by recent developments in sparse kernels, Bayesian generalized kernel inference is applied sequentially to the related problems of terrain elevation and traversability inference. The first inference step allows sparse data to support descriptive terrain modeling, and the second inference step relieves the burden typically associated with traversability computation. We explore the capabilities of the approach over a variety of data and terrain, demonstrating its suitability for online use in real-world applications.
more »
« less
- Award ID(s):
- 1723996
- PAR ID:
- 10113014
- Date Published:
- Journal Name:
- Proceedings of the 2nd Conference on Robot Learning
- Page Range / eLocation ID:
- 829 - 838
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Many differentially private algorithms for answering database queries involve a step that reconstructs a discrete data distribution from noisy measurements. This provides consistent query answers and reduces error, but often requires space that grows exponentially with dimension. Private-PGM is a recent approach that uses graphical models to represent the data distribution, with complexity proportional to that of exact marginal inference in a graphical model with structure determined by the co-occurrence of variables in the noisy measurements. Private-PGM is highly scalable for sparse measurements, but may fail to run in high dimensions with dense measurements. We overcome the main scalability limitation of Private-PGM through a principled approach that relaxes consistency constraints in the estimation objective. Our new approach works with many existing private query answering algorithms and improves scalability or accuracy with no privacy cost.more » « less
-
n this article, we present a novel and flexible multitask multilayer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for robots in a single mapping formalism while exploiting intralayer and interlayer correlations. It removes the need for a robot to access and process information from many separate maps when performing a complex task, advancing the way robots interact with their environments. To this end, we design a multitask deep neural network with attention mechanisms as our front-end to provide heterogeneous observations for multiple map layers simultaneously. Our back-end runs a scalable closed-form Bayesian inference with only logarithmic time complexity. We apply the framework to build a dense robotic map, including metric-semantic occupancy and traversability layers. Traversability ground truth labels are automatically generated from exteroceptive sensory data in a self-supervised manner. We present extensive experimental results on publicly available datasets and data collected by a three-dimensional bipedal robot platform and show reliable mapping performance in different environments. Finally, we also discuss how the current framework can be extended to incorporate more information, such as friction, signal strength, temperature, and physical quantity concentration using Gaussian map layers. The software for reproducing the presented results or running on customized data is made publicly available.more » « less
-
Traditional ground vehicle architectures comprise of a chassis connected via passive, semi-active, or active suspension systems to multiple ground wheels. Current design-optimizations of vehicle architectures for on-road applications have diminished their mobility and maneuverability in off-road settings. Autonomous Ground Vehicles (AGV) traversing off-road environments face numerous challenges concerning terrain roughness, soil hardness, uneven obstacle-filled terrain, and varying traction conditions. Numerous Active Articulated-Wheeled (AAW) vehicle architectures have emerged to permit AGVs to adapt to variable terrain conditions in various off-road application arenas (off-road, construction, mining, and space robotics). However, a comprehensive framework of AAW platforms for exploring various facets of system architecture/design, analysis (kinematics/dynamics), and control (motions/forces) remains challenging. While current literature on the AAW system incorporates modeling and control from the legged and wheeled-legged robots community, it lacks a systematic process of architecture selection and motion control that should be developed around critical quantifiable performance parameters. This paper will: (i) analyze a broad body of literature; and (ii) identify modeling and control techniques that can enable the efficient development of AAW platforms. We then analyze key performance measures with respect to traversability, maneuverability, and terrainability, along with an experimental simulation of an AAW vehicle traversing over uneven terrain and how active articulation could achieve some of the critical performance measures. Against the performance parameters, gaps within the existing literature and opportunities for further research are identified to potentially enhance AAW platforms’ performance.more » « less
-
Gaussian processes (GPs) offer a flexible class of priors for nonparametric Bayesian regression, but popular GP posterior inference methods are typically prohibitively slow or lack desirable finite-data guarantees on quality. We develop a scalable approach to approximate GP regression, with finite-data guarantees on the accuracy of our pointwise posterior mean and variance estimates. Our main contribution is a novel objective for approximate inference in the nonparametric setting: the preconditioned Fisher (pF) divergence. We show that unlike the Kullback–Leibler divergence (used in variational inference), the pF divergence bounds bounds the 2-Wasserstein distance, which in turn provides tight bounds on the pointwise error of mean and variance estimates. We demonstrate that, for sparse GP likelihood approximations, we can minimize the pF divergence bounds efficiently. Our experiments show that optimizing the pF divergence bounds has the same computational requirements as variational sparse GPs while providing comparable empirical performance—in addition to our novel finite-data quality guarantees.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
