Karthik Dantu

Kimia Khabiri, Parsa Hosseininejad, Shishir Gopinath et al.

The tracking module of a visual-inertial SLAM system processes incoming image frames and IMU data to estimate the position of the frame in relation to the map. It is important for the tracking to complete in a timely manner for each frame to avoid poor localization or tracking loss. We therefore present a new approach which leverages GPU computing power to accelerate time-consuming components of tracking in order to improve its performance. These components include stereo feature matching and local map tracking. We implement our design inside the ORB-SLAM3 tracking process using CUDA. Our evaluation demonstrates an overall improvement in tracking performance of up to 2.8x on a desktop and Jetson Xavier NX board in stereo-inertial mode, using the well-known SLAM datasets EuRoC and TUM-VI.

Timothy Chase, Chris Gnam, John Crassidis et al.

The detection of hazardous terrain during the planetary landing of spacecraft plays a critical role in assuring vehicle safety and mission success. A cheap and effective way of detecting hazardous terrain is through the use of visual cameras, which ensure operational ability from atmospheric entry through touchdown. Plagued by resource constraints and limited computational power, traditional techniques for visual hazardous terrain detection focus on template matching and registration to pre-built hazard maps. Although successful on previous missions, this approach is restricted to the specificity of the templates and limited by the fidelity of the underlying hazard map, which both require extensive pre-flight cost and effort to obtain and develop. Terrestrial systems that perform a similar task in applications such as autonomous driving utilize state-of-the-art deep learning techniques to successfully localize and classify navigation hazards. Advancements in spacecraft co-processors aimed at accelerating deep learning inference enable the application of these methods in space for the first time. In this work, we introduce You Only Crash Once (YOCO), a deep learning-based visual hazardous terrain detection and classification technique for autonomous spacecraft planetary landings. Through the use of unsupervised domain adaptation we tailor YOCO for training by simulation, removing the need for real-world annotated data and expensive mission surveying phases. We further improve the transfer of representative terrain knowledge between simulation and the real world through visual similarity clustering. We demonstrate the utility of YOCO through a series of terrestrial and extraterrestrial simulation-to-real experiments and show substantial improvements toward the ability to both detect and accurately classify instances of planetary terrain.

Prajit KrisshnaKumar, Jhoel Witter, Steve Paul et al.

Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-off/landing and battery-recharging spots. Since they might be situated in dense urban areas and need to handle many aircraft landings and take-offs each hour, managing this schedule in real-time becomes challenging for a traditional air-traffic controller but instead calls for an automated solution. This paper provides a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies. Here the designated physical spots within the vertiport's airspace and the vehicles being managed are represented as two separate graphs, with feature extraction performed through a graph convolutional network (GCN). Extracted features are passed onto perceptron layers to decide actions such as continue to hover or cruise, continue idling or take-off, or land on an allocated vertiport spot. Performance is measured based on delays, safety (no. of collisions) and battery consumption. Through realistic simulations in AirSim applied to scaled down multi-rotor vehicles, our results demonstrate the suitability of using graph reinforcement learning to solve the UAM-VSM problem and its superiority to basic reinforcement learning (with graph embeddings) or random choice baselines.

Yuyang Chen, Praveen Raj Masilamani, Bhavin Jawade et al.

In recent times, there is an increased interest in the identification and re-identification of people at long distances, such as from rooftop cameras, UAV cameras, street cams, and others. Such recognition needs to go beyond face and use whole-body markers such as gait. However, datasets to train and test such recognition algorithms are not widely prevalent, and fewer are labeled. This paper introduces DIOR -- a framework for data collection, semi-automated annotation, and also provides a dataset with 14 subjects and 1.649 million RGB frames with 3D/2D skeleton gait labels, including 200 thousands frames from a long range camera. Our approach leverages advanced 3D computer vision techniques to attain pixel-level accuracy in indoor settings with motion capture systems. Additionally, for outdoor long-range settings, we remove the dependency on motion capture systems and adopt a low-cost, hybrid 3D computer vision and learning pipeline with only 4 low-cost RGB cameras, successfully achieving precise skeleton labeling on far-away subjects, even when their height is limited to a mere 20-25 pixels within an RGB frame. On publication, we will make our pipeline open for others to use.

Yash Turkar, Pranay Meshram, Christo Aluckal et al.

Advancements in sensors, algorithms, and compute hardware have made 3D perception feasible in real time. Current methods to compare and evaluate the quality of a 3D model, such as Chamfer, Hausdorff, and Earth-Mover's distance, are uni-dimensional and have limitations, including an inability to capture coverage, local variations in density and error, and sensitivity to outliers. In this paper, we propose an evaluation framework for point clouds (Empir3D) that consists of four metrics: resolution to quantify the ability to distinguish between individual parts in the point cloud, accuracy to measure registration error, coverage to evaluate the portion of missing data, and artifact score to characterize the presence of artifacts. Through detailed analysis, we demonstrate the complementary nature of each of these dimensions and the improvements they provide compared to the aforementioned uni-dimensional measures. Furthermore, we illustrate the utility of Empir3D by comparing our metrics with uni-dimensional metrics for two 3D perception applications (SLAM and point cloud completion). We believe that Empir3D advances our ability to reason about point clouds and helps better debug 3D perception applications by providing a richer evaluation of their performance. Our implementation of Empir3D, custom real-world datasets, evaluations on learning methods, and detailed documentation on how to integrate the pipeline will be made available upon publication.

Xiaozhou Liang, John Henry Burns, Joseph Sanchez et al.

Unmanned Aerial Vehicles (UAVs) are an emerging computation platform known for their safety-critical need. In this paper, we conduct an empirical study on a widely used open-source UAV software framework, Paparazzi, with the goal of understanding the safety-critical concerns of UAV software from a bottom-up developer-in-the-field perspective. We set our focus on the use of Bounding Functions (BFs), the runtime checks injected by Paparazzi developers on the range of variables. Through an in-depth analysis on BFs in the Paparazzi autopilot software, we found a large number of them (109 instances) are used to bound safety-critical variables essential to the cyber-physical nature of the UAV, such as its thrust, its speed, and its sensor values. The novel contributions of this study are two fold. First, we take a static approach to classify all BF instances, presenting a novel datatype-based 5-category taxonomy with fine-grained insight on the role of BFs in ensuring the safety of UAV systems. Second, we dynamically evaluate the impact of the BF uses through a differential approach, establishing the UAV behavioral difference with and without BFs. The two-pronged static and dynamic approach together illuminates a rarely studied design space of safety-critical UAV software systems.

Yash Turkar, Timothy Chase, Christo Aluckal et al.

Accurate feature detection is fundamental for various computer vision tasks, including autonomous robotics, 3D reconstruction, medical imaging, and remote sensing. Despite advancements in enhancing the robustness of visual features, no existing method measures the utility of visual information before processing by specific feature-type algorithms. To address this gap, we introduce PIXER and the concept of "Featureness," which reflects the inherent interest and reliability of visual information for robust recognition, independent of any specific feature type. Leveraging a generalization on Bayesian learning, our approach quantifies both the probability and uncertainty of a pixel's contribution to robust visual utility in a single-shot process, avoiding costly operations such as Monte Carlo sampling and permitting customizable featureness definitions adaptable to a wide range of applications. We evaluate PIXER on visual odometry with featureness selectivity, achieving an average of 31% improvement in RMSE trajectory with 49% fewer features.

Timothy Chase, Christopher Wilson, Karthik Dantu

The in-situ detection of planetary, lunar, and small-body surface terrain is crucial for autonomous spacecraft applications, where learning-based computer vision methods are increasingly employed to enable intelligence without prior information or human intervention. However, many of these methods remain computationally expensive for spacecraft processors and prevent real-time operation. Training of such algorithms is additionally complex due to the scarcity of labeled data and reliance on supervised learning approaches. Unsupervised Domain Adaptation (UDA) offers a promising solution by facilitating model training with disparate data sources such as simulations or synthetic scenes, although UDA is difficult to apply to celestial environments where challenging feature spaces are paramount. To alleviate such issues, You Only Crash Once (YOCOv1) has studied the integration of Visual Similarity-based Alignment (VSA) into lightweight one-stage object detection architectures to improve space terrain UDA. Although proven effective, the approach faces notable limitations, including performance degradations in multi-class and high-altitude scenarios. Building upon the foundation of YOCOv1, we propose novel additions to the VSA scheme that enhance terrain detection capabilities under UDA, and our approach is evaluated across both simulated and real-world data. Our second YOCO rendition, YOCOv2, is capable of achieving state-of-the-art UDA performance on surface terrain detection, where we showcase improvements upwards of 31% compared with YOCOv1 and terrestrial state-of-the-art. We demonstrate the practical utility of YOCOv2 with spacecraft flight hardware performance benchmarking and qualitative evaluation of NASA mission data.

Timothy Chase, Karthik Dantu

The detection and tracking of celestial surface terrain features are crucial for autonomous spaceflight applications, including Terrain Relative Navigation (TRN), Entry, Descent, and Landing (EDL), hazard analysis, and scientific data collection. Traditional photoclinometry-based pipelines often rely on extensive a priori imaging and offline processing, constrained by the computational limitations of radiation-hardened systems. While historically effective, these approaches typically increase mission costs and duration, operate at low processing rates, and have limited generalization. Recently, learning-based computer vision has gained popularity to enhance spacecraft autonomy and overcome these limitations. While promising, emerging techniques frequently impose computational demands exceeding the capabilities of typical spacecraft hardware for real-time operation and are further challenged by the scarcity of labeled training data for diverse extraterrestrial environments. In this work, we present novel formulations for in-situ landmark tracking via detection and description. We utilize lightweight, computationally efficient neural network architectures designed for real-time execution on current-generation spacecraft flight processors. For landmark detection, we propose improved domain adaptation methods that enable the identification of celestial terrain features with distinct, cheaply acquired training data. Concurrently, for landmark description, we introduce a novel attention alignment formulation that learns robust feature representations that maintain correspondence despite significant landmark viewpoint variations. Together, these contributions form a unified system for landmark tracking that demonstrates superior performance compared to existing state-of-the-art techniques.

Yash Turkar, Youngjin Kim, Karthik Dantu

Subterranean environments such as culverts present significant challenges to robot vision due to dim lighting and lack of distinctive features. Although onboard illumination can help, it introduces issues such as specular reflections, overexposure, and increased power consumption. We propose NightHawk, a framework that combines active illumination with exposure control to optimize image quality in these settings. NightHawk formulates an online Bayesian optimization problem to determine the best light intensity and exposure-time for a given scene. We propose a novel feature detector-based metric to quantify image utility and use it as the cost function for the optimizer. We built NightHawk as an event-triggered recursive optimization pipeline and deployed it on a legged robot navigating a culvert beneath the Erie Canal. Results from field experiments demonstrate improvements in feature detection and matching by 47-197% enabling more reliable visual estimation in challenging lighting conditions.

Vaishali Maheshkar, Aadarsh Anantha Ramakrishnan, Charuvahan Adhivarahan et al.

According to the EPA, only 25% of waste is recycled, and just 60% of U.S. municipalities offer curbside recycling. Plastics fare worse, with a recycling rate of only 8%; an additional 16% is incinerated, while the remaining 76% ends up in landfills. The low plastic recycling rate stems from contamination, poor economic incentives, and technical difficulties, making efficient recycling a challenge. To improve recovery, automated sorting plays a critical role. Companies like AMP Robotics and Greyparrot utilize optical systems for sorting, while Materials Recovery Facilities (MRFs) employ Near-Infrared (NIR) sensors to detect plastic types. Modern optical sorting uses advances in computer vision such as object recognition and instance segmentation, powered by machine learning. Two-stage detectors like Mask R-CNN use region proposals and classification with deep backbones like ResNet. Single-stage detectors like YOLO handle detection in one pass, trading some accuracy for speed. While such methods excel under ideal conditions with a large volume of labeled training data, challenges arise in realistic scenarios, emphasizing the need to further examine the efficacy of optic detection for automated sorting. In this study, we compiled novel datasets totaling 20,000+ images from varied sources. Using both public and custom machine learning pipelines, we assessed the capabilities and limitations of optical recognition for sorting. Grad-CAM, saliency maps, and confusion matrices were employed to interpret model behavior. We perform this analysis on our custom trained models from the compiled datasets. To conclude, our findings are that optic recognition methods have limited success in accurate sorting of real-world plastics at MRFs, primarily because they rely on physical properties such as color and shape.

Joseph P. Distefano, Hemanth Manjunatha, Souma Chowdhury et al.

Human-swarm interaction has recently gained attention due to its plethora of new applications in disaster relief, surveillance, rescue, and exploration. However, if the task difficulty increases, the performance of the human operator decreases, thereby decreasing the overall efficacy of the human-swarm team. Thus, it is critical to identify the task difficulty and adaptively allocate the task to the human operator to maintain optimal performance. In this direction, we study the classification of task difficulty in a human-swarm interaction experiment performing a target search mission. The human may control platoons of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to search a partially observable environment during the target search mission. The mission complexity is increased by introducing adversarial teams that humans may only see when the environment is explored. While the human is completing the mission, their brain activity is recorded using an electroencephalogram (EEG), which is used to classify the task difficulty. We have used two different approaches for classification: A feature-based approach using coherence values as input and a deep learning-based approach using raw EEG as input. Both approaches can classify the task difficulty well above the chance. The results showed the importance of the occipital lobe (O1 and O2) coherence feature with the other brain regions. Moreover, we also study individual differences (expert vs. novice) in the classification results. The analysis revealed that the temporal lobe in experts (T4 and T3) is predominant for task difficulty classification compared with novices.

Amir Behjat, Hemanth Manjunatha, Prajit KrisshnaKumar et al.

To accomplish complex swarm robotic missions in the real world, one needs to plan and execute a combination of single robot behaviors, group primitives such as task allocation, path planning, and formation control, and mission-specific objectives such as target search and group coverage. Most such missions are designed manually by teams of robotics experts. Recent work in automated approaches to learning swarm behavior has been limited to individual primitives with sparse work on learning complete missions. This paper presents a systematic approach to learn tactical mission-specific policies that compose primitives in a swarm to accomplish the mission efficiently using neural networks with special input and output encoding. To learn swarm tactics in an adversarial environment, we employ a combination of 1) map-to-graph abstraction, 2) input/output encoding via Pareto filtering of points of interest and clustering of robots, and 3) learning via neuroevolution and policy gradient approaches. We illustrate this combination as critical to providing tractable learning, especially given the computational cost of simulating swarm missions of this scale and complexity. Successful mission completion outcomes are demonstrated with up to 60 robots. In addition, a close match in the performance statistics in training and testing scenarios shows the potential generalizability of the proposed framework.

Leighton Collins, Payam Ghassemi, Ehsan T. Esfahani et al.

This paper presents a novel multi-robot coverage path planning (CPP) algorithm - aka SCoPP - that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e.g., no-fly zones) in a specified area of interest, and provides an optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user's input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.

Nagashri Lakshminarayana, Yifang Liu, Karthik Dantu et al.

This paper describes a system by which Unmanned Aerial Vehicles (UAVs) can gather high-quality face images that can be used in biometric identification tasks. Success in face-based identification depends in large part on the image quality, and a major factor is how frontal the view is. Face recognition software pipelines can improve identification rates by synthesizing frontal views from non-frontal views by a process call {\em frontalization}. Here we exploit the high mobility of UAVs to actively gather frontal images using components of a synthetic frontalization pipeline. We define a frontalization error and show that it can be used to guide an UAVs to capture frontal views. Further, we show that the resulting image stream improves matching quality of a typical face recognition similarity metric. The system is implemented using an off-the-shelf hardware and software components and can be easily transfered to any ROS enabled UAVs.