EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy

The field of keypoint extraction, which is essential for vision applications like Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM), has evolved from relying on handcrafted methods to leveraging deep learning techniques. While deep learning approaches have significantly improved performance, they often incur substantial computational costs, limiting their deployment in real-time edge applications. Efforts to create lightweight neural networks have seen some success, yet they often result in trade-offs between efficiency and accuracy. Additionally, the high-dimensional descriptors generated by these networks poses challenges for distributed applications requiring efficient communication and coordination, highlighting the need for compact yet competitively accurate descriptors. In this paper, we present EdgePoint2, a series of lightweight keypoint detection and description neural networks specifically tailored for edge computing applications on embedded system. The network architecture is optimized for efficiency without sacrificing accuracy. To train compact descriptors, we introduce a combination of Orthogonal Procrustes loss and similarity loss, which can serve as a general approach for hypersphere embedding distillation tasks. Additionally, we offer 14 sub-models to satisfy diverse application requirements. Our experiments demonstrate that EdgePoint2 consistently achieves state-of-the-art (SOTA) accuracy and efficiency across various challenging scenarios while employing lower-dimensional descriptors (32/48/64). Beyond its accuracy, EdgePoint2 offers significant advantages in flexibility, robustness, and versatility. Consequently, EdgePoint2 emerges as a highly competitive option for visual tasks, especially in contexts demanding adaptability to diverse computational and communication constraints.

Distributed Consistent Multi-robot Cooperative Localization: A Coordinate Transformation Approach

This paper considers the problem of distributed cooperative localization (CL) via robot-to-robot measurements for a multi-robot system. We propose a distributed consistent CL algorithm. The key idea is to perform the EKF-based state estimation in a transformed coordinate system. Specifically, a coordinate transformation is constructed by decomposing the state-propagation Jacobian by which the correct observability properties are guaranteed. Moreover, the transformed state-propagation Jacobian becomes an identity matrix which is more suitable for distribution. In the proposed algorithm, a server-based framework is adopted to distributely estimate the robot pose in which each robot propagates its pose estimations and the server maintains the correlations. To reduce communication costs, only when the multi-robot system takes a robot-to-robot relative measurement, the robots and the server exchange information to update the pose estimations and the correlations. In addition, no assumptions are made about the type of robots or relative measurements. The proposed algorithm has been validated by experiments and shown to outperform the state-of-art algorithms in terms of consistency and accuracy.