Event-based Simultaneous Localization and Mapping: A Comprehensive Survey (2304.09793v2)

Abstract: In recent decades, visual simultaneous localization and mapping (vSLAM) has gained significant interest in both academia and industry. It estimates camera motion and reconstructs the environment concurrently using visual sensors on a moving robot. However, conventional cameras are limited by hardware, including motion blur and low dynamic range, which can negatively impact performance in challenging scenarios like high-speed motion and high dynamic range illumination. Recent studies have demonstrated that event cameras, a new type of bio-inspired visual sensor, offer advantages such as high temporal resolution, dynamic range, low power consumption, and low latency. This paper presents a timely and comprehensive review of event-based vSLAM algorithms that exploit the benefits of asynchronous and irregular event streams for localization and mapping tasks. The review covers the working principle of event cameras and various event representations for preprocessing event data. It also categorizes event-based vSLAM methods into four main categories: feature-based, direct, motion-compensation, and deep learning methods, with detailed discussions and practical guidance for each approach. Furthermore, the paper evaluates the state-of-the-art methods on various benchmarks, highlighting current challenges and future opportunities in this emerging research area. A public repository will be maintained to keep track of the rapid developments in this field at {\url{https://github.com/kun150kun/ESLAM-survey}}.

• The paper demonstrates that event-based vSLAM overcomes frame-based limitations by leveraging asynchronous event sensors for improved motion and dynamic range performance.
• It categorizes methods into feature-based, direct, motion-compensation, and deep learning, detailing unique algorithmic adaptations and sensor fusion strategies.
• Numerical evaluations on benchmark datasets reveal enhanced tracking accuracy and robustness in challenging environments, suggesting promising avenues for future research.

Event-based Simultaneous Localization and Mapping: A Comprehensive Survey

This comprehensive survey authored by Kunping Huang, Sen Zhang, Jing Zhang, and Dacheng Tao, explores the burgeoning research area of event-based simultaneous localization and mapping (vSLAM), leveraging the unique properties of event cameras. Unlike conventional frame-based cameras, event cameras operate asynchronously, capturing pixel-level brightness changes. This feature enables superior performance in high-speed motion and high dynamic range scenarios, thereby addressing several limitations of traditional cameras concerning motion blur and dynamic range.

The survey is systematically organized to offer an exhaustive overview of event-based vSLAM, categorized into four distinct methodological approaches: feature-based, direct, motion-compensation, and deep learning methods. Each category provides insights into the processing and application of event data for localization and mapping tasks.

Feature-based Methods are primarily concerned with the extraction and tracking of features such as corner points or lines from event data. The paper highlights that while traditional corner detection algorithms can be adapted, event cameras' reliance on motion intricacies mean that novel algorithmic designs or the fusion of sensor data are often necessary to achieve robust performance. The inclusion of additional sensor inputs such as IMU data can significantly enhance tracking accuracy in challenging conditions, as demonstrated in extended temporal aggregation techniques.

Direct Methods bypass explicit feature detection by aligning event data directly to brightness intensities or edge structures within pre-processed event representations. These approaches benefit from high temporal resolution and perform uncoupled tracking and mapping through dense image-like reconstructions of raw event data. Direct methods excel in maintaining computational efficiency and robustness, particularly in texture-starved environments, though their accuracy may degrade with extreme motion dynamics.

Motion-compensation Methods employ innovative strategies like contrast maximization and probabilistic models to account for the impact of motion across multiple frames, essentially warping event data to reduce distortion or blurring over time. By compensating for motion artifacts, these methods ensure that events are well aligned spatially and temporally, leading to sharper image reconstruction and enhanced localization accuracy. However, the phenomenon known as event collapse remains a technical challenge, limiting these methods' effectiveness under certain motion configurations.

Deep Learning Techniques leverage the adaptability of neural networks trained on synthetic or richly annotated datasets, providing a promising frontier for incorporating semantic understanding and achieving higher-level tasks. Self-supervised learning schemes, in particular, offer potential by adapting models using temporal dynamics and photometric cues independently of extensive ground truth data.

Numerical evaluations across benchmark datasets like the MVSEC and rpg datasets demonstrate these methodologies' varying strengths and limitations, substantiating the claim that event-based vSLAM systems possess significant advantages in high-speed and adverse environments. The depth of the survey is further enhanced by a call for continuous research into sensor noise modeling, event data sparsity, global optimization, and the integration of multi-sensor setups, such as combining events with inertial or frame-based data.

In reflecting on the future of event-based vSLAM, the authors advocate for the development of more robust event representations, scalable theoretical frameworks, and more reliable systems in dynamic or textureless environments. The synthesis of foundation models with multi-modal data promises to further unlock the potential of event cameras in applications demanding resilience to perceptual challenges.

In summary, this survey provides a seminal consolidation of the conceptual and practical landscape surrounding event-based vSLAM, fostering a nuanced understanding of how these emerging methodologies can transform computational perception and navigation systems under dynamic, real-world conditions.