Is That Rain? Understanding Effects on Visual Odometry Performance for Autonomous UAVs and Efficient DNN-based Rain Classification at the Edge

• URL: http://arxiv.org/abs/2407.12663v1
• Date: Wed, 17 Jul 2024 15:47:25 GMT
• Title: Is That Rain? Understanding Effects on Visual Odometry Performance for Autonomous UAVs and Efficient DNN-based Rain Classification at the Edge
• Authors: Andrea Albanese, Yanran Wang, Davide Brunelli, David Boyle,
• Abstract summary: State-of-the-art local tracking and trajectory planning are typically performed using camera sensor input to the flight control algorithm. We show that a worst-case average tracking error of 1.5 m is possible for a state-of-the-art visual odometry system. We train a set of deep neural network models suited to mobile and constrained deployment scenarios to determine the extent to which it may be possible to efficiently and accurately classify these rainy' conditions.
• Score: 1.8936798735951972
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The development of safe and reliable autonomous unmanned aerial vehicles relies on the ability of the system to recognise and adapt to changes in the local environment based on sensor inputs. State-of-the-art local tracking and trajectory planning are typically performed using camera sensor input to the flight control algorithm, but the extent to which environmental disturbances like rain affect the performance of these systems is largely unknown. In this paper, we first describe the development of an open dataset comprising ~335k images to examine these effects for seven different classes of precipitation conditions and show that a worst-case average tracking error of 1.5 m is possible for a state-of-the-art visual odometry system (VINS-Fusion). We then use the dataset to train a set of deep neural network models suited to mobile and constrained deployment scenarios to determine the extent to which it may be possible to efficiently and accurately classify these `rainy' conditions. The most lightweight of these models (MobileNetV3 small) can achieve an accuracy of 90% with a memory footprint of just 1.28 MB and a frame rate of 93 FPS, which is suitable for deployment in resource-constrained and latency-sensitive systems. We demonstrate a classification latency in the order of milliseconds using typical flight computer hardware. Accordingly, such a model can feed into the disturbance estimation component of an autonomous flight controller. In addition, data from unmanned aerial vehicles with the ability to accurately determine environmental conditions in real time may contribute to developing more granular timely localised weather forecasting.

Related papers

• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving [0.0]
  Environmental perception is a key element of autonomous driving. In this paper, we investigate the best trade-off between detection quality and latency for real-time perception in autonomous vehicles. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.
  arXiv  Detail & Related papers  (2023-08-09T21:39:10Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR [0.0]
  The study reveals that decision tree (DT) and random forest give reasonably similar macro average f1-scores to predict a person using sensor data. DT is the most reliable predictive model for the cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%.
  arXiv  Detail & Related papers  (2022-04-10T11:39:46Z)
• How Do We Fail? Stress Testing Perception in Autonomous Vehicles [40.19326157052966]
  This paper presents a method for characterizing failures of LiDAR-based perception systems for autonomous vehicles in adverse weather conditions. We develop a methodology based in reinforcement learning to find likely failures in object tracking and trajectory prediction due to sequences of disturbances.
  arXiv  Detail & Related papers  (2022-03-26T20:48:09Z)
• Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy [48.51396198176273]
  We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
  arXiv  Detail & Related papers  (2021-09-14T07:24:53Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
  We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
  arXiv  Detail & Related papers  (2021-04-22T12:47:37Z)
• Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors [99.94079901071163]
  This paper presents a Dynamic Vision Sensor (DVS) based system for reasoning about high speed motion. We consider the case of a robot at rest reacting to a small, fast approaching object at speeds higher than 15m/s. We highlight the results of our system to a toy dart moving at 23.4m/s with a 24.73deg error in $theta$, 18.4mm average discretized radius prediction error, and 25.03% median time to collision prediction error.
  arXiv  Detail & Related papers  (2020-11-18T17:55:07Z)
• Streaming Object Detection for 3-D Point Clouds [29.465873948076766]
  LiDAR provides a prominent sensory modality that informs many existing perceptual systems. The latency for perceptual systems based on point cloud data can be dominated by the amount of time for a complete rotational scan. We show how operating on LiDAR data in its native streaming formulation offers several advantages for self driving object detection.
  arXiv  Detail & Related papers  (2020-05-04T21:55:15Z)

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.