Uncertainty-Aware Deep Learning for Autonomous Safe Landing Site Selection

• URL: http://arxiv.org/abs/2102.10545v1
• Date: Sun, 21 Feb 2021 08:13:49 GMT
• Title: Uncertainty-Aware Deep Learning for Autonomous Safe Landing Site Selection
• Authors: Kento Tomita and Katherine A. Skinner and Koki Ho
• Abstract summary: This paper proposes an uncertainty-aware learning-based method for hazard detection and landing site selection. It generates a safety prediction map and its uncertainty map together via Bayesian deep learning and semantic segmentation. It uses the generated uncertainty map to filter out the uncertain pixels in the prediction map so that the safe landing site selection is performed only based on the certain pixels.
• Score: 3.996275177789895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. At the same time, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has been often limited due to concerns regarding their outputs' reliability. In response to this background, this paper proposes an uncertainty-aware learning-based method for hazard detection and landing site selection. The developed approach enables reliable safe landing site selection by: (i) generating a safety prediction map and its uncertainty map together via Bayesian deep learning and semantic segmentation; and (ii) using the generated uncertainty map to filter out the uncertain pixels in the prediction map so that the safe landing site selection is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model and varying noise levels to demonstrate the performance of the proposed approach.

Related papers

• Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications [1.8294777056635267]
  Vehicle systems need reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method's performance using traffic-related datasets.
  arXiv  Detail & Related papers  (2024-10-09T14:08:39Z)
• Generative Edge Detection with Stable Diffusion [52.870631376660924]
  Edge detection is typically viewed as a pixel-level classification problem mainly addressed by discriminative methods. We propose a novel approach, named Generative Edge Detector (GED), by fully utilizing the potential of the pre-trained stable diffusion model. We conduct extensive experiments on multiple datasets and achieve competitive performance.
  arXiv  Detail & Related papers  (2024-10-04T01:52:23Z)
• Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing [0.0]
  This paper develops a novel real-time planetary terrain mapping algorithm. It accounts for topographic uncertainty between the sampled points, or the uncertainty due to sparse 3D measurements.
  arXiv  Detail & Related papers  (2024-09-14T05:12:14Z)
• Visual Environment Assessment for Safe Autonomous Quadrotor Landing [8.538463567092297]
  We present a novel approach for detection and assessment of potential landing sites for safe quadrotor landing. Our solution efficiently integrates 2D and 3D environmental information, eliminating the need for external aids such as GPS. Our approach runs in real-time on quadrotors equipped with limited computational capabilities.
  arXiv  Detail & Related papers  (2023-11-16T18:02:10Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• Deep Monocular Hazard Detection for Safe Small Body Landing [12.922946578413578]
  Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image. We demonstrate precise and accurate safety mapping performance on real in-situ imagery of prospective sample sites from the OSIRIS-REx mission.
  arXiv  Detail & Related papers  (2023-01-30T19:40:46Z)
• Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction [50.79827516897913]
  We introduce an interpretable paradigm for trajectory prediction that distributes the uncertainty among semantic concepts. We validate our approach on real-world autonomous driving data, demonstrating superior performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-11-16T06:28:20Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics [0.0]
  Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. High level terrain classification is not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap. Online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover.
  arXiv  Detail & Related papers  (2020-09-21T21:56:44Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)

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.