Event-based Sensor Fusion and Application on Odometry: A Survey

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness, offering notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this paper presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly, investigating fusion strategies that incorporate frame-based cameras, inertial measurement units (IMUs), and LiDAR. The survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications.

LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation

Keypoint detection and description play a pivotal role in various robotics and autonomous applications including visual odometry (VO), visual navigation, and Simultaneous localization and mapping (SLAM). While a myriad of keypoint detectors and descriptors have been extensively studied in conventional camera images, the effectiveness of these techniques in the context of LiDAR-generated images, i.e. reflectivity and ranges images, has not been assessed. These images have gained attention due to their resilience in adverse conditions such as rain or fog. Additionally, they contain significant textural information that supplements the geometric information provided by LiDAR point clouds in the point cloud registration phase, especially when reliant solely on LiDAR sensors. This addresses the challenge of drift encountered in LiDAR Odometry (LO) within geometrically identical scenarios or where not all the raw point cloud is informative and may even be misleading. This paper aims to analyze the applicability of conventional image key point extractors and descriptors on LiDAR-generated images via a comprehensive quantitative investigation. Moreover, we propose a novel approach to enhance the robustness and reliability of LO. After extracting key points, we proceed to downsample the point cloud, subsequently integrating it into the point cloud registration phase for the purpose of odometry estimation. Our experiment demonstrates that the proposed approach has comparable accuracy but reduced computational overhead, higher odometry publishing rate, and even superior performance in scenarios prone to drift by using the raw point cloud. This, in turn, lays a foundation for subsequent investigations into the integration of LiDAR-generated images with LO. Our code is available on GitHub: https://github.com/TIERS/ws-lidar-as-camera-odom.