Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoor applications and for close proximity to humans. To enable autonomy, the nano-UAV must be able to self-localize in its operating environment. This is a particularly-challenging task due to the limited sensing and compute resources on board. This work presents an online and onboard approach for localization in floor plans annotated with semantic information. Unlike sensor-based maps, floor plans are readily-available, and do not increase the cost and time of deployment. To overcome the difficulty of localizing in sparse maps, the proposed approach fuses geometric information from miniaturized time-of-flight sensors and semantic cues. The semantic information is extracted from images by deploying a state-of-the-art object detection model on a high-performance multi-core microcontroller onboard the drone, consuming only 2.5mJ per frame and executing in 38ms. In our evaluation, we globally localize in a real-world office environment, achieving 90% success rate. We also release an open-source implementation of our work1.

Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems, allowing for more efficient planning and execution of tasks. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines.1

Nano-size unmanned aerial vehicles (UAVs) hold enormous potential to perform autonomous operations in complex environments, such as inspection, monitoring or data collection. Moreover, their small size allows safe operation close to humans and agile flight. An important part of autonomous flight is localization, a computationally intensive task, especially on a nano-UAV that usually has strong constraints in sensing, processing and memory. This work presents a real-time localization approach with low-element-count multizone range sensors for resource-constrained nano-UAVs. The proposed approach is based on a novel miniature 64-zone time-of-flight sensor from STMicroelectronics and a RISC-V-based parallel ultra-low-power processor to enable accurate and low latency Monte Carlo Localization on-board. Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31.2 m2map with 0.15 m accuracy and an above 95% success rate. The achieved accuracy is sufficient for localization in common indoor environments. We analyze trade-offs in using full and half-precision floating point numbers as well as a quantized map and evaluate the accuracy and memory footprint across the design space. Experimental evaluation shows that parallelizing the execution for 8 RISC-V cores brings a 7x speedup and allows us to execute the algorithm onboard in real-time with a latency of 0.2-30 ms (depending on the number of particles), while only increasing the overall drone power consumption by 3–7%. Finally, we provide an open-source implementation of our approach.

Object-based maps are relevant for scene under-standing since they integrate geometric and semantic information of the environment, allowing autonomous robots to robustly localize and interact with on objects. In this paper, we address the task of constructing a metric-semantic map for the purpose of long-term object-based localization. We exploit 3D object detections from monocular RGB frames for both, the object-based map construction, and for globally localizing in the constructed map. To tailor the approach to a target environment, we propose an efficient way of generating 3D annotations to finetune the 3D object detection model. We evaluate our map construction in an office building, and test our long-term localization approach on challenging sequences recorded in the same environment over nine months. The experiments suggest that our approach is suitable for constructing metric-semantic maps, and that our localization approach is robust to long-term changes. Both, the mapping algorithm and the localization pipeline can run online on an onboard computer. We release an open-source C++/ros implementation of our approach.

Robust localization in a given map is a crucial component of most autonomous robots. In this paper, we address the problem of localizing in an indoor environment that changes and where prominent structures have no correspondence in the map built at a different point in time. To overcome the discrepancy between the map and the observed environment caused by such changes, we exploit human-readable localization cues to assist localization. These cues are readily available in most facilities and can be detected using RGB camera images by utilizing text spotting. We integrate these cues into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. By this, we provide a robust localization solution for environments with structural changes and dynamics by humans walking. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We release an open source implementation of our approach11https://github.com/PRBonn/tmcl, which uses off-the-shelf text spotting, written in C++ with a ROS wrapper.

Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few cm2form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget (~100mW). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV’s relative 3-D pose with respect to a person while they freely move in the environment—a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called Frontnet, designed for deployment on top of a parallel ultra-low power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to135 frame/s), consuming less than87 mWfor processing at peak throughput and down to0.43 mJ/framein the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-g Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard:41cm, ideal:26 cm) and angular (onboard:3.7°, ideal:4.1°). We publicly release videos and the source code of our work.

Deep neural networks (DNNs) are becoming the first-class solution for autonomous unmanned aerial vehicles (UAVs) applications, especially for tiny, resource-constrained, nano-UAVs, with a few tens of grams in weight and subten centimeters in diameter. DNN visual pipelines have been proven capable of delivering high intelligence aboard nanoUAVs, efficiently exploiting novel multi-core microcontroller units. However, one severe limitation of this class of solutions is the generalization challenge, i.e., the visual cues learned on the specific training domain hardly predict with the same accuracy on different ones. Ultimately, it results in very limited applicability of State-of-the-Art (SoA) autonomous navigation DNNs outside controlled environments. In this work, we tackle this problem in the context of the human pose estimation task with a SoA vision-based DNN [1]. We propose a novel methodology that leverages synthetic domain randomization by applying a simple but effective image background replacement technique to augment our training dataset. Our results demonstrate how the augmentation forces the learning process to focus on what matters most: the pose of the human subject. Our approach reduces the DNN’s mean square error — vs. a non-augmented baseline — by up to 40%, on a never-seen-before testing environment. Since our methodology tackles the DNN’s training stage, the improved generalization capabilities come at zero-cost for the computational/memory burdens aboard the nano-UAV.