The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing
Flight and Robotic Perception
- URL: http://arxiv.org/abs/2101.10371v2
- Date: Thu, 18 Feb 2021 18:31:48 GMT
- Title: The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing
Flight and Robotic Perception
- Abstract summary: flapping-wing robot perception suffers from high vibration levels and abrupt movements during flight.
This paper presents a perception dataset for bird-scale flapping-wing robots.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The development of automatic perception systems and techniques for
bio-inspired flapping-wing robots is severely hampered by the high technical
complexity of these platforms and the installation of onboard sensors and
electronics. Besides, flapping-wing robot perception suffers from high
vibration levels and abrupt movements during flight, which cause motion blur
and strong changes in lighting conditions. This paper presents a perception
dataset for bird-scale flapping-wing robots as a tool to help alleviate the
aforementioned problems. The presented data include measurements from onboard
sensors widely used in aerial robotics and suitable to deal with the perception
challenges of flapping-wing robots, such as an event camera, a conventional
camera, and two Inertial Measurement Units (IMUs), as well as ground truth
measurements from a laser tracker or a motion capture system. A total of 21
datasets of different types of flights were collected in three different
scenarios (one indoor and two outdoor). To the best of the authors' knowledge
this is the first dataset for flapping-wing robot perception.
Related papers
- EVPeriscope: Extended Perception across Aerial and Ground Vehicles with Event-based Propeller Tracking [20.43225691472079]
This paper presents an event-based perception system that enables detection, localization and control of a quadrotor using an upward-facing event camera on a ground robot.<n>We show that the system supports localization and closed-loop navigation through dense foliage where the ground robot's sensors are occluded.
arXiv Detail & Related papers (2026-09-10T17:58:03Z) - Controlled Flight of an Insect-Scale Flapping-Wing Robot via Integrated Onboard Sensing and Computation [16.63542876415974]
We present a 1.29-gram aerial robot capable of hovering and tracking trajectories with solely onboard sensing and computation.<n>In a 30-second flight experiment conducted outside a motion capture system, the robot avoided obstacles and ultimately landed on a sunflower.
arXiv Detail & Related papers (2026-02-09T07:03:53Z) - RobotSeg: A Model and Dataset for Segmenting Robots in Image and Video [56.9581053843815]
We introduce RobotSeg, a foundation model for robot segmentation in image and video.<n>It addresses the lack of adaptation to articulated robots, reliance on manual prompts, and the need for per-frame training mask annotations.<n>It achieves state-of-the-art performance on both images and videos.
arXiv Detail & Related papers (2025-11-28T07:51:02Z) - Real-Time Glass Detection and Reprojection using Sensor Fusion Onboard Aerial Robots [9.34137124377529]
transparent obstacles present significant challenges to reliable navigation and mapping.<n>We propose a novel and computationally efficient framework for detecting and mapping transparent obstacles onboard a sub-300g quadrotor.<n>Our method fuses data from a Time-of-Flight (ToF) camera and an ultrasonic sensor with a custom, lightweight 2D convolution model.<n>The entire pipeline operates in real-time, utilizing only a small fraction of a CPU core on an embedded processor.
arXiv Detail & Related papers (2025-10-07T23:31:45Z) - Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation [50.34179054785646]
We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed.
Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs.
These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development.
arXiv Detail & Related papers (2025-04-17T12:57:11Z) - RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments [62.5830455357187]
We setup an egocentric multi-sensor data collection platform based on 3 main types of sensors (Camera, LiDAR and Fisheye)
A large-scale multimodal dataset is constructed, named RoboSense, to facilitate egocentric robot perception.
arXiv Detail & Related papers (2024-08-28T03:17:40Z) - EchoPT: A Pretrained Transformer Architecture that Predicts 2D In-Air Sonar Images for Mobile Robotics [4.103476395113937]
This paper introduces EchoPT, a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information.
In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks.
arXiv Detail & Related papers (2024-05-21T08:18:28Z) - Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip
Perception of Mobile Manipulation Robots [22.63980025871784]
The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor.
The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status.
Anomalies can then be identified by error scores measured by the difference between the trained encoder's latent values and the latent values of reconstructed input data.
arXiv Detail & Related papers (2024-03-06T09:15:53Z) - USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for
Heavy-duty Autonomous Aerial Work Robots [13.089952067224138]
We present the USTC FLICAR dataset, which is dedicated to the development of simultaneous localization and mapping.
The proposed dataset extends the typical autonomous driving sensing suite to aerial scenes.
Based on the Segment Anything Model (SAM), we produce the Semantic FLICAR dataset, which provides fine-grained semantic segmentation annotations.
arXiv Detail & Related papers (2023-04-04T17:45:06Z) - Autonomous Marker-less Rapid Aerial Grasping [5.892028494793913]
We propose a vision-based system for autonomous rapid aerial grasping.
We generate a dense point cloud of the detected objects and perform geometry-based grasp planning.
We show the first use of geometry-based grasping techniques with a flying platform.
arXiv Detail & Related papers (2022-11-23T16:25:49Z) - Neural Scene Representation for Locomotion on Structured Terrain [56.48607865960868]
We propose a learning-based method to reconstruct the local terrain for a mobile robot traversing urban environments.
Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the estimates the topography in the robot's vicinity.
We propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement.
arXiv Detail & Related papers (2022-06-16T10:45:17Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - CNN-based Omnidirectional Object Detection for HermesBot Autonomous
Delivery Robot with Preliminary Frame Classification [53.56290185900837]
We propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification.
An autonomous mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup.
arXiv Detail & Related papers (2021-10-22T15:05:37Z) - 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) - 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)
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.