Bearing-only Simultaneous Localization and Mapping for Vision-Based Mobile Robots

Huang, Haochu; Maire, Frédéric; Keeratipranon, Narongdech

doi:10.5772/4996

Cited by 2 publications

(15 citation statements)

References 26 publications

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“…Consequently, the objective has shifted to enabling each event to independently update the system's state asynchronously [9]. Utilizing event cameras' asynchrony and high temporal resolution, SLAM algorithms can benefit from reduced motion blur and improved visual perception in dynamic environments, ultimately leading to more robust and accurate mapping and localization results [19]. It can enhance the reconstruction of 3D scenes and enable tracking of fast motion with high precision.…”

Section: Event Camera-based Slammentioning

confidence: 99%

“…It can enhance the reconstruction of 3D scenes and enable tracking of fast motion with high precision. Furthermore, a low data rate and reduced power consumption compared to traditional cameras make them ideal for resource-constrained devices in applications such as autonomous vehicles, robotics, and augmented reality [19]. Moreover, it can be used to significantly increase the frame rate of low-framerate video while occupying significantly less memory space than conventional camera frames, enabling efficient and superior-quality video frame interpolation [22].…”

Section: Event Camera-based Slammentioning

confidence: 99%

“…The advent of novel concepts and the production of bio-inspired visual sensors and processors through developments in neuroscience and neuromorphic technologies have brought a radical change in the processes of artificial visual systems [9,16,17]. An event camera (also known as a Dynamic Vision Sensor (DVS) or neuromorphic camera) operates very differently from conventional frame-based cameras; it only generates an output (in the form of timestamped events or spikes) when there are changes in the brightness of a scene [9,12,16,18,19]. Compared to regular cameras, event cameras have a greater dynamic range, reduced latency, higher temporal resolution, and significantly lower power consumption and bandwidth usage [3,9,12,13,17,18,[20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…The use of event cameras allows SLAM systems to better handle dynamic situations and fast motion without being affected by motion blur or illumination variations. Event cameras provide high dynamic range imagery and low latency through asynchronous pixel-level brightness change capture [9,12,16,18,19]. Additionally, neuromorphic processors emulate the brain's structure and functionality [25], enabling efficient and highly parallel processing, which is particularly advantageous for real-time SLAM operations on embedded devices.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, [11,28,32,33] have covered neuromorphic computing technology and its challenges, however, no clear direction towards its integration into event-based SLAM was provided. Review papers [19,22,[34][35][36][37][38] have mentioned the methods and models to be employed in SLAM but did not discuss the combined approach.…”

Section: Introductionmentioning

confidence: 99%

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Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

Tenzin,

Rassau,

Chai

2024

Preprint

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Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.

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Section: Event Camera-based Slammentioning

confidence: 99%

Section: Event Camera-based Slammentioning

confidence: 99%