arXiv subjects

Aamir Ahmad

Publications and source records attributed to Aamir Ahmad.

34 records · Page 2Linked to original sources

arXiv2023

SmartMocap: Joint Estimation of Human and Camera Motion using Uncalibrated RGB Cameras

Markerless human motion capture (mocap) from multiple RGB cameras is a widely studied problem. Existing methods either need calibrated cameras or calibrate them relative to a static camera, which acts as the reference frame for the mocap system. The calibration step has to be done a priori for every capture session, which is a tedious process, and re-calibration is required whenever cameras are intentionally or accidentally moved. In this paper, we propose a mocap method which uses multiple static and moving extrinsically uncalibrated RGB cameras. The key components of our method are as follows. First, since the cameras and the subject can move freely, we select the ground plane as a common reference to represent both the body and the camera motions unlike existing methods which represent bodies in the camera coordinate. Second, we learn a probability distribution of short human motion sequences ($\sim$1sec) relative to the ground plane and leverage it to disambiguate between the camera and human motion. Third, we use this distribution as a motion prior in a novel multi-stage optimization approach to fit the SMPL human body model and the camera poses to the human body keypoints on the images. Finally, we show that our method can work on a variety of datasets ranging from aerial cameras to smartphones. It also gives more accurate results compared to the state-of-the-art on the task of monocular human mocap with a static camera. Our code is available for research purposes on https://github.com/robot-perception-group/SmartMocap.

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arXiv2023

Autonomous Blimp Control via H-infinity Robust Deep Residual Reinforcement Learning

Due to their superior energy efficiency, blimps may replace quadcopters for long-duration aerial tasks. However, designing a controller for blimps to handle complex dynamics, modeling errors, and disturbances remains an unsolved challenge. One recent work combines reinforcement learning (RL) and a PID controller to address this challenge and demonstrates its effectiveness in real-world experiments. In the current work, we build on that using an H-infinity robust controller to expand the stability margin and improve the RL agent's performance. Empirical analysis of different mixing methods reveals that the resulting H-infinity-RL controller outperforms the prior PID-RL combination and can handle more complex tasks involving intensive thrust vectoring. We provide our code as open-source at https://github.com/robot-perception-group/robust_deep_residual_blimp.

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arXiv2023

Viewpoint-driven Formation Control of Airships for Cooperative Target Tracking

For tracking and motion capture (MoCap) of animals in their natural habitat, a formation of safe and silent aerial platforms, such as airships with on-board cameras, is well suited. In our prior work we derived formation properties for optimal MoCap, which include maintaining constant angular separation between observers w.r.t. the subject, threshold distance to it and keeping it centered in the camera view. Unlike multi-rotors, airships have non-holonomic constrains and are affected by ambient wind. Their orientation and flight direction are also tightly coupled. Therefore a control scheme for multicopters that assumes independence of motion direction and orientation is not applicable. In this paper, we address this problem by first exploiting a periodic relationship between the airspeed of an airship and its distance to the subject. We use it to derive analytical and numeric solutions that satisfy the formation properties for optimal MoCap. Based on this, we develop an MPC-based formation controller. We perform theoretical analysis of our solution, boundary conditions of its applicability, extensive simulation experiments and a real world demonstration of our control method with an unmanned airship. Open source code https://tinyurl.com/AsMPCCode and a video of our method is provided at https://tinyurl.com/AsMPCVid .

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arXiv2023

Accelerated Video Annotation driven by Deep Detector and Tracker

Annotating object ground truth in videos is vital for several downstream tasks in robot perception and machine learning, such as for evaluating the performance of an object tracker or training an image-based object detector. The accuracy of the annotated instances of the moving objects on every image frame in a video is crucially important. Achieving that through manual annotations is not only very time consuming and labor intensive, but is also prone to high error rate. State-of-the-art annotation methods depend on manually initializing the object bounding boxes only in the first frame and then use classical tracking methods, e.g., adaboost, or kernelized correlation filters, to keep track of those bounding boxes. These can quickly drift, thereby requiring tedious manual supervision. In this paper, we propose a new annotation method which leverages a combination of a learning-based detector (SSD) and a learning-based tracker (RE$^3$). Through this, we significantly reduce annotation drifts, and, consequently, the required manual supervision. We validate our approach through annotation experiments using our proposed annotation method and existing baselines on a set of drone video frames. Source code and detailed information on how to run the annotation program can be found at https://github.com/robot-perception-group/smarter-labelme

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arXiv2022

iRotate: Active Visual SLAM for Omnidirectional Robots

In this paper, we present an active visual SLAM approach for omnidirectional robots. The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consuming as low energy as possible. Leveraging the robot's independent translation and rotation control, we introduce a multi-layered approach for active V-SLAM. The top layer decides on informative goal locations and generates highly informative paths to them. The second and third layers actively re-plan and execute the path, exploiting the continuously updated map and local features information. Moreover, we introduce two utility formulations to account for the presence of obstacles in the field of view and the robot's location. Through rigorous simulations, real robot experiments, and comparisons with state-of-the-art methods, we demonstrate that our approach achieves similar coverage results with lesser overall map entropy. This is obtained while keeping the traversed distance up to 39% shorter than the other methods and without increasing the wheels' total rotation amount. Code and implementation details are provided as open-source, and all the generated data is available on-line for consultation.

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arXiv2022

Deep Residual Reinforcement Learning based Autonomous Blimp Control

Blimps are well suited to perform long-duration aerial tasks as they are energy efficient, relatively silent and safe. To address the blimp navigation and control task, in previous work we developed a hardware and software-in-the-loop framework and a PID-based controller for large blimps in the presence of wind disturbance. However, blimps have a deformable structure and their dynamics are inherently non-linear and time-delayed, making PID controllers difficult to tune. Thus, often resulting in large tracking errors. Moreover, the buoyancy of a blimp is constantly changing due to variations in ambient temperature and pressure. To address these issues, in this paper we present a learning-based framework based on deep residual reinforcement learning (DRRL), for the blimp control task. Within this framework, we first employ a PID controller to provide baseline performance. Subsequently, the DRRL agent learns to modify the PID decisions by interaction with the environment. We demonstrate in simulation that DRRL agent consistently improves the PID performance. Through rigorous simulation experiments, we show that the agent is robust to changes in wind speed and buoyancy. In real-world experiments, we demonstrate that the agent, trained only in simulation, is sufficiently robust to control an actual blimp in windy conditions. We openly provide the source code of our approach at https://github.com/ robot-perception-group/AutonomousBlimpDRL.

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arXiv2022

AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation

In this letter, we present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments that uses a team of autonomous unmanned aerial vehicles (UAVs) with on-board RGB cameras and computation. Existing methods are limited by calibrated cameras and off-line processing. Thus, we present the first method (AirPose) to estimate human pose and shape using images captured by multiple extrinsically uncalibrated flying cameras. AirPose itself calibrates the cameras relative to the person instead of relying on any pre-calibration. It uses distributed neural networks running on each UAV that communicate viewpoint-independent information with each other about the person (i.e., their 3D shape and articulated pose). The person's shape and pose are parameterized using the SMPL-X body model, resulting in a compact representation, that minimizes communication between the UAVs. The network is trained using synthetic images of realistic virtual environments, and fine-tuned on a small set of real images. We also introduce an optimization-based post-processing method (AirPose$^{+}$) for offline applications that require higher MoCap quality. We make our method's code and data available for research at https://github.com/robot-perception-group/AirPose. A video describing the approach and results is available at https://youtu.be/xLYe1TNHsfs.

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arXiv2021

Autonomous Blimp Control using Deep Reinforcement Learning

Aerial robot solutions are becoming ubiquitous for an increasing number of tasks. Among the various types of aerial robots, blimps are very well suited to perform long-duration tasks while being energy efficient, relatively silent and safe. To address the blimp navigation and control task, in our recent work, we have developed a software-in-the-loop simulation and a PID-based controller for large blimps in the presence of wind disturbance. However, blimps have a deformable structure and their dynamics are inherently non-linear and time-delayed, often resulting in large trajectory tracking errors. Moreover, the buoyancy of a blimp is constantly changing due to changes in the ambient temperature and pressure. In the present paper, we explore a deep reinforcement learning (DRL) approach to address these issues. We train only in simulation, while keeping conditions as close as possible to the real-world scenario. We derive a compact state representation to reduce the training time and a discrete action space to enforce control smoothness. Our initial results in simulation show a significant potential of DRL in solving the blimp control task and robustness against moderate wind and parameter uncertainty. Extensive experiments are presented to study the robustness of our approach. We also openly provide the source code of our approach.

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arXiv2021

Active Visual SLAM with Independently Rotating Camera

In active Visual-SLAM (V-SLAM), a robot relies on the information retrieved by its cameras to control its own movements for autonomous mapping of the environment. Cameras are usually statically linked to the robot's body, limiting the extra degrees of freedom for visual information acquisition. In this work, we overcome the aforementioned problem by introducing and leveraging an independently rotating camera on the robot base. This enables us to continuously control the heading of the camera, obtaining the desired optimal orientation for active V-SLAM, without rotating the robot itself. However, this additional degree of freedom introduces additional estimation uncertainties, which need to be accounted for. We do this by extending our robot's state estimate to include the camera state and jointly estimate the uncertainties. We develop our method based on a state-of-the-art active V-SLAM approach for omnidirectional robots and evaluate it through rigorous simulation and real robot experiments. We obtain more accurate maps, with lower energy consumption, while maintaining the benefits of the active approach with respect to the baseline. We also demonstrate how our method easily generalizes to other non-omnidirectional robotic platforms, which was a limitation of the previous approach. Code and implementation details are provided as open-source.

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arXiv2021

Collaborative Mapping of Archaeological Sites using multiple UAVs

UAVs have found an important application in archaeological mapping. Majority of the existing methods employ an offline method to process the data collected from an archaeological site. They are time-consuming and computationally expensive. In this paper, we present a multi-UAV approach for faster mapping of archaeological sites. Employing a team of UAVs not only reduces the mapping time by distribution of coverage area, but also improves the map accuracy by exchange of information. Through extensive experiments in a realistic simulation (AirSim), we demonstrate the advantages of using a collaborative mapping approach. We then create the first 3D map of the Sadra Fort, a 15th Century Fort located in Gujarat, India using our proposed method. Additionally, we present two novel archaeological datasets recorded in both simulation and real-world to facilitate research on collaborative archaeological mapping. For the benefit of the community, we make the AirSim simulation environment, as well as the datasets publicly available.

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arXiv2020

Simulation and Control of Deformable Autonomous Airships in Turbulent Wind

Abstract. Fixed wing and multirotor UAVs are common in the field of robotics. Solutions for simulation and control of these vehicles are ubiquitous. This is not the case for airships, a simulation of which needs to address unique properties, i) dynamic deformation in response to aerodynamic and control forces, ii) high susceptibility to wind and turbulence at low airspeed, iii) high variability in airship designs regarding placement, direction and vectoring of thrusters and control surfaces. We present a flexible framework for modeling, simulation and control of airships, based on the Robot operating system (ROS), simulation environment (Gazebo) and commercial off the shelf (COTS) electronics, both of which are open source. Based on simulated wind and deformation, we predict substantial effects on controllability, verified in real world flight experiments. All our code is shared as open source, for the benefit of the community and to facilitate lighter-than-air vehicle (LTAV) research. https://github.com/robot-perception-group/airship_simulation

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arXiv2020

AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning

In this letter, we introduce a deep reinforcement learning (RL) based multi-robot formation controller for the task of autonomous aerial human motion capture (MoCap). We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose and shape of a single moving person using multiple micro aerial vehicles. State-of-the-art solutions to this problem are based on classical control methods, which depend on hand-crafted system and observation models. Such models are difficult to derive and generalize across different systems. Moreover, the non-linearity and non-convexities of these models lead to sub-optimal controls. In our work, we formulate this problem as a sequential decision making task to achieve the vision-based motion capture objectives, and solve it using a deep neural network-based RL method. We leverage proximal policy optimization (PPO) to train a stochastic decentralized control policy for formation control. The neural network is trained in a parallelized setup in synthetic environments. We performed extensive simulation experiments to validate our approach. Finally, real-robot experiments demonstrate that our policies generalize to real world conditions. Video Link: https://bit.ly/38SJfjo Supplementary: https://bit.ly/3evfo1O

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arXiv2019

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M}

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arXiv2019

Active Perception based Formation Control for Multiple Aerial Vehicles

Autonomous motion capture (mocap) systems for outdoor scenarios involving flying or mobile cameras rely on i) a robotic front-end to track and follow a human subject in real-time while he/she performs physical activities, and ii) an algorithmic back-end that estimates full body human pose and shape from the saved videos. In this paper we present a novel front-end for our aerial mocap system that consists of multiple micro aerial vehicles (MAVs) with only on-board cameras and computation. In previous work, we presented an approach for cooperative detection and tracking (CDT) of a subject using multiple MAVs. However, it did not ensure optimal view-point configurations of the MAVs to minimize the uncertainty in the person's cooperatively tracked 3D position estimate. In this article we introduce an active approach for CDT. In contrast to cooperatively tracking only the 3D positions of the person, the MAVs can now actively compute optimal local motion plans, resulting in optimal view-point configurations, which minimize the uncertainty in the tracked estimate. We achieve this by decoupling the goal of active tracking as a convex quadratic objective and non-convex constraints corresponding to angular configurations of the MAVs w.r.t. the person. We derive it using Gaussian observation model assumptions within the CDT algorithm. We also show how we embed all the non-convex constraints, including those for dynamic and static obstacle avoidance, as external control inputs in the MPC dynamics. Multiple real robot experiments and comparisons involving 3 MAVs in several challenging scenarios are presented (video link : https://youtu.be/1qWW2zWvRhA). Extensive simulation results demonstrate the scalability and robustness of our approach. ROS-based source code is also provided.

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arXiv2019

Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems

In this paper, a kinematic motion planning algorithm for cooperative spatial payload manipulation is presented. A hierarchical approach is introduced to compute real-time collision-free motion plans for a formation of mobile manipulator robots. Initially, collision-free configurations of a deformable 2-D virtual bounding box are identified, over a planning horizon, to define a convex workspace for the entire system. Then, 3-D payload configurations whose projections lie within the defined convex workspace are computed. Finally, a convex decentralized model-predictive controller is formulated to plan collision-free trajectories for the formation of mobile manipulators. This approach facilitates real-time motion planning for the system and is scalable in the number of robots. The algorithm is validated in simulated dynamic environments. Simulation video: https://youtu.be/9EKj7RwRs_4.

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arXiv2018

Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles

Multi-camera full-body pose capture of humans and animals in outdoor environments is a highly challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. The key enabling-aspect of our approach is the on-board person detection and tracking method. Recent state-of-the-art methods based on deep neural networks (DNN) are highly promising in this context. However, real time DNNs are severely constrained in input data dimensions, in contrast to available camera resolutions. Therefore, DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, real-time, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs. First, each MAV fuses its own detections with those obtained by other MAVs to perform cooperative visual tracking. This allows for predicting future poses of the tracked person, which are used to selectively process only the relevant regions of future images, even at high resolutions. Consequently, using our DNN-based detector we are able to continuously track even distant humans with high accuracy and speed. We demonstrate the efficiency of our approach through real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. Our solution runs fully on-board our MAV's CPU and GPU, with no remote processing. ROS-based source code is provided for the benefit of the community.

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