Abstract:Quadrotor usage is continuously increasing for both civilian and military applications such as surveillance, mapping, and deliveries. Commonly, quadrotors use an inertial navigation system combined with a global navigation satellite systems receiver for outdoor applications and a camera for indoor/outdoor applications. For various reasons, such as lighting conditions or satellite signal blocking, the quadrotor’s navigation solution depends only on the inertial navigation system solution. As a consequence, the … Show more
“… QDR only offers a position solution (p2p) between two subsequent peaks, with a time interval of several hundred milliseconds between the two peaks. To overcome those shortcomings, deep-learning QDR approaches were suggested 31 yet they are not the main topic of this paper. …”
Quadrotors have found widespread use in indoor applications, including tracking and mapping. In general, to carry out such tasks effectively, a navigation solution should provide both accuracy and battery efficiency. To achieve both, we propose a cost-effective and lightweight wheeled quadrotor that combines both driving and flying capabilities. Our design allows the quadrotor to perform both functions seamlessly. We provide a detailed description of the design and construction process, highlighting its advantages. Our focus was on the Tello quadrotor, which weighs 80 grams. Our design allowed driving capability with an increased weight of only fifteen grams, resulting in less than 20% of the added weight. Furthermore, we evaluate the quadrotor’s pure inertial navigation performance and corresponding battery consumption by employing various flying and driving patterns. Our results show that when only driving the battery consumption was the lowest with 10% and some flying scenarios improve the positioning error by more than 70%.
“… QDR only offers a position solution (p2p) between two subsequent peaks, with a time interval of several hundred milliseconds between the two peaks. To overcome those shortcomings, deep-learning QDR approaches were suggested 31 yet they are not the main topic of this paper. …”
Quadrotors have found widespread use in indoor applications, including tracking and mapping. In general, to carry out such tasks effectively, a navigation solution should provide both accuracy and battery efficiency. To achieve both, we propose a cost-effective and lightweight wheeled quadrotor that combines both driving and flying capabilities. Our design allows the quadrotor to perform both functions seamlessly. We provide a detailed description of the design and construction process, highlighting its advantages. Our focus was on the Tello quadrotor, which weighs 80 grams. Our design allowed driving capability with an increased weight of only fifteen grams, resulting in less than 20% of the added weight. Furthermore, we evaluate the quadrotor’s pure inertial navigation performance and corresponding battery consumption by employing various flying and driving patterns. Our results show that when only driving the battery consumption was the lowest with 10% and some flying scenarios improve the positioning error by more than 70%.
“…As a consequence, the navigation solution drifts in time due to errors and noises in the inertial sensor measurements. To handle such situations and bind the solution drift, we developed QuadNet, a hybrid framework for quadrotor dead reckoning to estimate the quadrotor's three-dimensional position vector at any user-defined time rate [17].…”
The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to modelbased methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approaches.
“…Research aimed at minimizing cumulative errors to enhance the accuracy of DR is actively being conducted across various fields [14][15][16][17][18]. Shurin et al (2022) proposed pedestrian DR to address the drift issue over time caused by errors and noise in inertial sensor measurements in the navigation of quadcopters [14]. Jang et al (2024) developed a position and direction estimation algorithm for active driving of wheelchairs using ultrawideband (UWB) sensors [15].…”
This paper proposes a dead-reckoning (DR) method for vehicles using Lie theory. This approach treats the pose (position and attitude) and velocity of the vehicle as elements of the Lie group SE2(3) and follows the computations based on Lie theory. Previously employed DR methods, which have been widely used, suffer from cumulative errors over time due to inaccuracies in the calculated changes from velocity during the motion of the vehicle or small errors in modeling assumptions. Consequently, this results in significant discrepancies between the estimated and actual positions over time. However, by treating the pose and velocity of the vehicle as elements of the Lie group, the proposed method allows for accurate solutions without the errors introduced by linearization. The incremental updates for pose and velocity in the DR computation are represented in the Lie algebra. Experimental results confirm that the proposed method improves the accuracy of DR. In particular, as the motion prediction time interval of the vehicle increases, the proposed method demonstrates a more pronounced improvement in positional accuracy.
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