This article focuses on Group Target Tracking (GTT) to counter swarms of drones using Random Finite Sets (RFSs) and Random Matrix (RM) approaches. Tracking swarms of drones is analog to tracking extended targets that are characterized by their continuously evolving shape and composition. Extended target tracking for groups of targets finds various applications in the literature because detecting and tracking each individual target of a group is computationally demanding and unnecessary if the group itself can be modeled. Elliptic shapes offers a suitable representation for most groups, and their inference is quite inexpensive with the random matrix approach. Indeed, they are efficient when coupled with random finite sets based filters, which represents the current state of the art for Bayesian multi-target tracking. In this work, a practical implementation of a labeled Poisson/Multi-Bernoulli filter using random matrices for group target tracking is proposed. This study compares several random matrix prediction and update algorithms with and without random finite sets based filters on several dataset.

We propose a complete loop (detection, estimation, avoidance) for the safe navigation of an autonomous vehicle in presence of dynamical obstacles. For detecting moving objects from stereo images and estimating their positions, two algorithms are proposed. The first one is dense and has a high computational load but is designed to fully exploit GPU processing. The second one is lighter and can run on a standard embedded processor After a step of filtering, the estimated mobile objects are exploited in a model predictive control scheme for collision avoidance while tracking a reference trajectory. Experimental results with the complete loop are reported for a micro-air vehicle and a mobile robot in realistic situations, with everything computed on board.

Estimating the optical flow robustly in real-time is still a challenging issue as revealed by current KITTI benchmarks. We propose an original two-step method for fast and performant optical flow estimation from stereo vision. The first step is the prediction of the flow due to the ego-motion, efficiently conducted by stereo-matching and visual odometry. The correction step estimates the motion of mobile objects. Algorithmic choices are justified by empirical studies on real datasets. Our method achieves framerate processing on images of realistic size, and provides results comparable or better than methods having computation times one or two orders of magnitude higher.

This paper presents a mobile object detection algorithm which performs with two consecutive stereo images. Like most motion detection methods, the proposed one is based on dense stereo matching and optical flow (OF) estimation. Noting that the main computational cost of existing methods is related to the estimation of OF, we propose to use a fast algorithm based on Lucas–Kanade paradigm. We then derive a comprehensive uncertainty model by taking into account all the estimation errors occurring during the process. In contrast with most previous works, we rigorously expand the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the challenging KITTI dataset which demonstrates the effectiveness of the proposed approach.

This paper considers passive vision for robotics and focuses on devising a real-time process for moving object detection using a stereo rig. As several previous works, our method relies on the use of dense stereo and of optical flow. Observing that the main computational load of existing methods is related to the estimation of the optical flow, we propose to use a fast algorithm based on Lucas-Kanade's paradigm. We derive a new uncertainty model which explicitly takes into account all errors originating from each estimation step of the process. In contrast with most previous works, we describe a rigorous expansion of the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the KITTI dataset, which demonstrates the effectiveness of the proposed approach.