Exploring Event Camera-based Odometry for Planetary Robots

• URL: http://arxiv.org/abs/2204.05880v1
• Date: Tue, 12 Apr 2022 15:19:50 GMT
• Title: Exploring Event Camera-based Odometry for Planetary Robots
• Authors: Florian Mahlknecht, Daniel Gehrig, Jeremy Nash, Friedrich M. Rockenbauer, Benjamin Morrell, Jeff Delaune, Davide Scaramuzza
• Abstract summary: Event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions. Existing event-based visual-inertial odometry (VIO) algorithms either suffer from high tracking errors or are brittle. We introduce EKLT-VIO, which addresses both limitations by combining a state-of-the-art event-based with a filter-based backend.
• Score: 39.46226359115717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions. However, existing event-based visual-inertial odometry (VIO) algorithms either suffer from high tracking errors or are brittle, since they cannot cope with significant depth uncertainties caused by an unforeseen loss of tracking or other effects. In this work, we introduce EKLT-VIO, which addresses both limitations by combining a state-of-the-art event-based frontend with a filter-based backend. This makes it both accurate and robust to uncertainties, outperforming event- and frame-based VIO algorithms on challenging benchmarks by 32%. In addition, we demonstrate accurate performance in hover-like conditions (outperforming existing event-based methods) as well as high robustness in newly collected Mars-like and high-dynamic-range sequences, where existing frame-based methods fail. In doing so, we show that event-based VIO is the way forward for vision-based exploration on Mars.

Related papers

• ES-PTAM: Event-based Stereo Parallel Tracking and Mapping [11.801511288805225]
  Event cameras offer advantages to overcome the limitations of standard cameras. We propose a novel event-based stereo VO system by combining two ideas. We evaluate the system on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-28T07:56:28Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Cross-Modal Semi-Dense 6-DoF Tracking of an Event Camera in Challenging Conditions [29.608665442108727]
  Event-based cameras are bio-inspired visual sensors that perform well in HDR conditions and have high temporal resolution. The present work demonstrates the feasibility of purely event-based tracking if an alternative sensor is permitted for mapping. The method relies on geometric 3D-2D registration of semi-dense maps and events, and achieves highly reliable and accurate cross-modal tracking results.
  arXiv  Detail & Related papers  (2024-01-16T01:48:45Z)
• EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping [5.154689086578339]
  We propose EVI-SAM to tackle the problem of 6 DoF pose tracking and 3D reconstruction using monocular event camera. A novel event-based hybrid tracking framework is designed to estimate the pose, leveraging the robustness of feature matching and the precision of direct alignment. To the best of our knowledge, this is the first non-learning work to realize event-based dense mapping.
  arXiv  Detail & Related papers  (2023-12-19T07:39:45Z)
• On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing [69.34740063574921]
  This paper presents a methodology for generating event-based vision datasets from optimal landing trajectories. We construct sequences of photorealistic images of the lunar surface with the Planet and Asteroid Natural Scene Generation Utility. We demonstrate that the pipeline can generate realistic event-based representations of surface features by constructing a dataset of 500 trajectories.
  arXiv  Detail & Related papers  (2023-08-01T09:14:20Z)
• AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility [125.77396380698639]
  AVisT is a benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios. We benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes.
  arXiv  Detail & Related papers  (2022-08-14T17:49:37Z)
• A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions [0.0]
  This work proposes a robust relative localization system based on a fusion of neuromorphic vision-based measurements (NVBMs) and inertial measurements. The proposed system was tested in a variety of experiments and has outperformed state-of-the-art approaches in accuracy and range.
  arXiv  Detail & Related papers  (2022-06-23T08:39:05Z)
• Asynchronous Optimisation for Event-based Visual Odometry [53.59879499700895]
  Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range. We focus on event-based visual odometry (VO) We propose an asynchronous structure-from-motion optimisation back-end.
  arXiv  Detail & Related papers  (2022-03-02T11:28:47Z)
• Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions [49.79068659889639]
  Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by traversability. We present an advanced robust monocular odometry algorithm that uses efficient optical flow tracking. We also present a novel approach to estimate the current risk of scale drift based on a principal component analysis of the relative translation information matrix.
  arXiv  Detail & Related papers  (2021-09-12T12:52:20Z)

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.