Low-light environments have posed a formidable challenge for robust unmanned aerial vehicle (UAV) tracking even with state-of-the-art (SOTA) trackers since the poten-tial image features are hard to extract under adverse light conditions. Besides, due to the low visibility, accurate online selection of the object also becomes extremely difficult for human monitors to initialize UAV tracking in ground con-trol stations. To solve these problems, this work proposes a novel enhancer, i.e., HighlightNet, to light up potential objects for both human operators and UAV trackers. By employing Transformer, HighlightNet can adjust enhancement parameters according to global features and is thus adaptive for the illumination variation. Pixel-level range mask is introduced to make HighlightNet more focused on the enhancement of the tracking object and regions without light sources. Furthermore, a soft truncation mechanism is built to prevent background noise from being mistaken for crucial features. Evaluations on image enhancement benchmarks demonstrate HighlightNet has advantages in facilitating human perception. Experiments on the public UAVDark135 benchmark show that HightlightNet is more suitable for UAV tracking tasks than other state-of-the-art (SOTA) low-light enhancers. In addition, real-world tests on a typical UAV platform verify HightlightNet's practicability and efficiency in nighttime aerial tracking-related applications. The code and demo videos are available at https://github.com/vision4robotics/HighlightNet.

3D mapping is a fundamental task in robot applications. The traditional mapping methods mainly rely on spatial discretization, in which the amount of data that needs to be stored is large, and the representation ability is limited. As a continuous probability model, the Gaussian Mixture Model (GMM) has a small memory footprint and high-fidelity representation ability. Thus the GMM map is superior to discrete map representations in basic robot tasks such as navigation and localization. The general method of building GMM maps is the iterative Expectation-Maximization (EM) algorithm with K-means initialization. The EM and K-means algorithms are computation-intensive, making it challenging to meet real-time 30 fps mapping requirements on the embedded robot systems. This paper proposes a Gaussian mixture model mapping and navigation engine (GAME) on embedded FPGA to accelerate the mapping process. To achieve fully pipelined with minimal hardware resource cost, we design a unified dataflow and hardware architecture for both K-means and EM for GMM. We analyze different quantization strategies for higher parallelism and find a low-bit quantization method with mixed 8/16-bit data representation, bringing negligible loss in accuracy. Combining the unified dataflow and the mixed-bit data quantization, GAME enables real-time GMM mapping and navigation on embedded robots. The experimental results on ZCU102 show that our proposed hardware-software co-optimization framework on FPGA can run over 60× faster than on a GeForce 1080Ti GPU and over 490× faster than on an Nvidia Jetson TX2, and achieves 59 fps.

Collaborative exploration in an unknown environment without external positioning under limited communication is an essential task for multi-robot applications. For inter-robot positioning, various Distributed Simultaneous Localization and Mapping (DSLAM) systems share the Place Recognition (PR) descriptors and sensor data to estimate the relative pose between robots and merge robots’ maps. As maps are constantly shared among robots in exploration, we design a map-based DSLAM framework, which only shares the submaps, eliminating the transfer of PR descriptors and sensor data. Our framework saves 30% of total communication traffic. For exploration, each robot is assigned to get much unknown information about environments with paying little travel cost. As the number of sampled points increases, the goal would change back and forth among sampled frontiers, leading to the downgrade in exploration efficiency and the overlap of trajectories. We propose an exploration strategy based on Multi-robot Multi-target Potential Field (MMPF), which can eliminate goal’s back-and-forth changes, boosting the exploration efficiency by 1.03 ×∼1.62 × with 3 % ∼ 40 % travel cost saved. Our SubMap-based Multi-robot Exploration method (SMMR-Explore) is evaluated on both Gazebo simulator and real robots. The simulator and the exploration framework are published as an open-source ROS project at https://github.com/efc-robot/SMMR-Explore.