Laser2Vec: Similarity-based Retrieval for Robotic Perception Data
- URL: http://arxiv.org/abs/2007.15746v1
- Date: Thu, 30 Jul 2020 21:11:50 GMT
- Title: Laser2Vec: Similarity-based Retrieval for Robotic Perception Data
- Authors: Samer B. Nashed
- Abstract summary: This paper implements a system for storing 2D LiDAR data from many deployments cheaply and evaluating top-k queries for complete or partial scans efficiently.
We generate compressed representations of laser scans via a convolutional variational autoencoder and store them in a database.
We find our system accurately and efficiently identifies similar scans across a number of episodes where the robot encountered the same location.
- Score: 7.538482310185135
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As mobile robot capabilities improve and deployment times increase, tools to
analyze the growing volume of data are becoming necessary. Current
state-of-the-art logging, playback, and exploration systems are insufficient
for practitioners seeking to discover systemic points of failure in robotic
systems. This paper presents a suite of algorithms for similarity-based queries
of robotic perception data and implements a system for storing 2D LiDAR data
from many deployments cheaply and evaluating top-k queries for complete or
partial scans efficiently. We generate compressed representations of laser
scans via a convolutional variational autoencoder and store them in a database,
where a light-weight dense network for distance function approximation is run
at query time. Our query evaluator leverages the local continuity of the
embedding space to generate evaluation orders that, in expectation, dominate
full linear scans of the database. The accuracy, robustness, scalability, and
efficiency of our system is tested on real-world data gathered from dozens of
deployments and synthetic data generated by corrupting real data. We find our
system accurately and efficiently identifies similar scans across a number of
episodes where the robot encountered the same location, or similar indoor
structures or objects.
Related papers
- Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition [6.468510459310326]
We introduce the concept of sample space in place recognition and demonstrate how different sampling techniques affect the query process and overall performance.
We then present a novel sampling approach for LiDAR-based place recognition, which focuses on redundancy and information preservation in the hyper-dimensional descriptor space.
arXiv Detail & Related papers (2024-10-03T16:29:47Z) - Tiny Robotics Dataset and Benchmark for Continual Object Detection [6.4036245876073234]
This work introduces a novel benchmark to evaluate the continual learning capabilities of object detection systems in tiny robotic platforms.
Our contributions include: (i) Tiny Robotics Object Detection (TiROD), a comprehensive dataset collected using a small mobile robot, designed to test the adaptability of object detectors across various domains and classes; (ii) an evaluation of state-of-the-art real-time object detectors combined with different continual learning strategies on this dataset; and (iii) we publish the data and the code to replicate the results to foster continuous advancements in this field.
arXiv Detail & Related papers (2024-09-24T16:21:27Z) - MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic
Grasping via Physics-based Metaverse Synthesis [78.26022688167133]
We present a large-scale benchmark dataset for vision-driven robotic grasping via physics-based metaverse synthesis.
The proposed dataset contains 100,000 images and 25 different object types.
We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance.
arXiv Detail & Related papers (2021-12-29T17:23:24Z) - Are we ready for beyond-application high-volume data? The Reeds robot
perception benchmark dataset [3.781421673607643]
This paper presents a dataset, called Reeds, for research on robot perception algorithms.
The dataset aims to provide demanding benchmark opportunities for algorithms, rather than providing an environment for testing application-specific solutions.
arXiv Detail & Related papers (2021-09-16T23:21:42Z) - Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for
Multi-Robot Systems [92.26462290867963]
Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures.
We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
arXiv Detail & Related papers (2021-06-28T03:56:40Z) - Domain and Modality Gaps for LiDAR-based Person Detection on Mobile
Robots [91.01747068273666]
This paper studies existing LiDAR-based person detectors with a particular focus on mobile robot scenarios.
Experiments revolve around the domain gap between driving and mobile robot scenarios, as well as the modality gap between 3D and 2D LiDAR sensors.
Results provide practical insights into LiDAR-based person detection and facilitate informed decisions for relevant mobile robot designs and applications.
arXiv Detail & Related papers (2021-06-21T16:35:49Z) - Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic
Simultaneous Localization and Mapping [57.173793973480656]
We present the first fully distributed multi-robot system for dense metric-semantic SLAM.
Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors.
Kimera-Multi builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label.
arXiv Detail & Related papers (2020-11-08T21:38:12Z) - Task-relevant Representation Learning for Networked Robotic Perception [74.0215744125845]
This paper presents an algorithm to learn task-relevant representations of sensory data that are co-designed with a pre-trained robotic perception model's ultimate objective.
Our algorithm aggressively compresses robotic sensory data by up to 11x more than competing methods.
arXiv Detail & Related papers (2020-11-06T07:39:08Z) - PyODDS: An End-to-end Outlier Detection System with Automated Machine
Learning [55.32009000204512]
We present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support.
Specifically, we define the search space in the outlier detection pipeline, and produce a search strategy within the given search space.
It also provides unified interfaces and visualizations for users with or without data science or machine learning background.
arXiv Detail & Related papers (2020-03-12T03:30:30Z) - Piecewise linear regressions for approximating distance metrics [1.1241621778067437]
This paper presents a data structure that summarizes distances between configurations across a robot configuration space.
The paper explores the use of the data structure constructed for a single robot to provide a for challenging multi-robot motion planning problems.
arXiv Detail & Related papers (2020-02-27T22:23:58Z)
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.