Machine Vision based Sample-Tube Localization for Mars Sample Return
- URL: http://arxiv.org/abs/2103.09942v1
- Date: Wed, 17 Mar 2021 23:09:28 GMT
- Title: Machine Vision based Sample-Tube Localization for Mars Sample Return
- Authors: Shreyansh Daftry, Barry Ridge, William Seto, Tu-Hoa Pham, Peter
Ilhardt, Gerard Maggiolino, Mark Van der Merwe, Alex Brinkman, John Mayo,
Eric Kulczyski and Renaud Detry
- Abstract summary: A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA.
In this paper, we focus on the fetch part of the MSR, and more specifically the problem of autonomously detecting and localizing sample tubes deposited on the Martian surface.
We study two machine-vision based approaches: First, a geometry-driven approach based on template matching that uses hard-coded filters and a 3D shape model of the tube; and second, a data-driven approach based on convolutional neural networks (CNNs) and learned features.
- Score: 3.548901442158138
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A potential Mars Sample Return (MSR) architecture is being jointly studied by
NASA and ESA. As currently envisioned, the MSR campaign consists of a series of
3 missions: sample cache, fetch and return to Earth. In this paper, we focus on
the fetch part of the MSR, and more specifically the problem of autonomously
detecting and localizing sample tubes deposited on the Martian surface. Towards
this end, we study two machine-vision based approaches: First, a
geometry-driven approach based on template matching that uses hard-coded
filters and a 3D shape model of the tube; and second, a data-driven approach
based on convolutional neural networks (CNNs) and learned features.
Furthermore, we present a large benchmark dataset of sample-tube images,
collected in representative outdoor environments and annotated with ground
truth segmentation masks and locations. The dataset was acquired systematically
across different terrain, illumination conditions and dust-coverage; and
benchmarking was performed to study the feasibility of each approach, their
relative strengths and weaknesses, and robustness in the presence of adverse
environmental conditions.
Related papers
- Unsupervised Machine Learning for Detecting and Locating Human-Made Objects in 3D Point Cloud [4.325161601374467]
This research introduces a novel task: detecting and identifying human-made objects amidst natural tree structures.
The proposed methodology consists of three stages: ground filtering, local information extraction, and clustering.
Experimental results demonstrate that the proposed ground filtering method outperforms previous techniques.
arXiv Detail & Related papers (2024-10-25T23:20:57Z) - Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries.
We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images.
Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
arXiv Detail & Related papers (2024-07-26T06:29:09Z) - MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.
The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
arXiv Detail & Related papers (2024-06-13T17:59:30Z) - Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets.
We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
arXiv Detail & Related papers (2024-01-06T07:30:41Z) - MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based
Self-Supervised Pre-Training [58.07391711548269]
Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training.
Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training.
arXiv Detail & Related papers (2023-03-23T17:59:02Z) - Hardware-accelerated Mars Sample Localization via deep transfer learning
from photorealistic simulations [1.3075880857448061]
The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study.
It is expected the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols.
This work proposes a novel approach for the autonomous detection and pose estimation of the sample tubes.
arXiv Detail & Related papers (2022-06-06T14:05:25Z) - TerrainMesh: Metric-Semantic Terrain Reconstruction from Aerial Images
Using Joint 2D-3D Learning [20.81202315793742]
This paper develops a joint 2D-3D learning approach to reconstruct a local metric-semantic mesh at each camera maintained by a visual odometry algorithm.
The mesh can be assembled into a global environment model to capture the terrain topology and semantics during online operation.
arXiv Detail & Related papers (2022-04-23T05:18:39Z) - A spatio-temporal LSTM model to forecast across multiple temporal and
spatial scales [0.0]
This paper presents a novel-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets.
The framework was evaluated across multiple sensors and for three different oceanic variables: current speed, temperature, and dissolved oxygen.
arXiv Detail & Related papers (2021-08-26T16:07:13Z) - Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical
Understanding of Outdoor Scene [76.4183572058063]
We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks.
The dataset has been point-wisely annotated with both hierarchical and instance-based labels.
We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
arXiv Detail & Related papers (2020-08-11T19:10:32Z) - Benchmarking Unsupervised Object Representations for Video Sequences [111.81492107649889]
We compare the perceptual abilities of four object-centric approaches: ViMON, OP3, TBA and SCALOR.
Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking.
Our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.
arXiv Detail & Related papers (2020-06-12T09:37:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.