Hao Li

Special tags such as AprilTags that facilitate image processing and pattern recognition are useful in practical applications. In close and private environments, identity security is unlikely to be an issue because all involved AprilTags can be completely regulated. However, in open and public environments, identity security is no longer an issue that can be neglected. To handle potential harm caused by adversarial attacks, this note advocates utilization of adjustable AprilTags instead of fixed ones.

In the field of autonomous driving or robotics, simultaneous localization and mapping (SLAM) and multi-object tracking (MOT) are two fundamental problems and are generally applied separately. Solutions to SLAM and MOT usually rely on certain assumptions, such as the static environment assumption for SLAM and the accurate ego-vehicle pose assumption for MOT. But in complex dynamic environments, it is difficult or even impossible to meet these assumptions. Therefore, the SLAMMOT, i.e., simultaneous localization, mapping, and moving object tracking, integrated system of SLAM and object tracking, has emerged for autonomous vehicles in dynamic environments. However, many conventional SLAMMOT solutions directly perform data association on the predictions and detections for object tracking, but ignore their quality. In practice, inaccurate predictions caused by continuous multi-frame missed detections in temporary occlusion scenarios, may degrade the performance of tracking, thereby affecting SLAMMOT. To address this challenge, this paper presents a LiDAR SLAMMOT based on confidence-guided data association (Conf SLAMMOT) method, which tightly couples the LiDAR SLAM and the confidence-guided data association based multi-object tracking into a graph optimization backend for estimating the state of the ego-vehicle and objects simultaneously. The confidence of prediction and detection are applied in the factor graph-based multi-object tracking for its data association, which not only avoids the performance degradation caused by incorrect initial assignments in some filter-based methods but also handles issues such as continuous missed detection in tracking while also improving the overall performance of SLAMMOT. Various comparative experiments demonstrate the superior advantages of Conf SLAMMOT, especially in scenes with some missed detections.

The SLAMMOT, i.e. simultaneous localization, mapping, and moving object (detection and) tracking, represents an emerging technology for autonomous vehicles in dynamic environments. Such single-vehicle systems still have inherent limitations, such as occlusion issues. Inspired by SLAMMOT and rapidly evolving cooperative technologies, it is natural to explore cooperative simultaneous localization, mapping, moving object (detection and) tracking (C-SLAMMOT) to enhance state estimation for ego-vehicles and moving objects. C-SLAMMOT could significantly upgrade the single-vehicle performance by utilizing and integrating the shared information through communication among the multiple vehicles. This inevitably leads to a fundamental trade-off between performance and communication cost, especially in a scalable manner as the number of collaboration vehicles increases. To address this challenge, we propose a LiDAR-based communication-efficient C-SLAMMOT (CE C-SLAMMOT) method by determining the number of collaboration vehicles. In CE C-SLAMMOT, we adopt descriptor-based methods for enhancing ego-vehicle pose estimation and spatial confidence map-based methods for cooperative object perception, allowing for the continuous and dynamic selection of the corresponding critical collaboration vehicles and interaction content. This approach avoids the waste of precious communication costs by preventing the sharing of information from certain collaborative vehicles that may contribute little or no performance gain, compared to the baseline method of exchanging raw observation information among all vehicles. Comparative experiments in various aspects have confirmed that the proposed method achieves a good trade-off between performance and communication costs, while also outperforms previous state-of-the-art methods in cooperative perception performance.