# UWBike: Global Bicycle Positioning System Using a Single Ultra-Wideband Reference Point

Lars Mathuseck, Lennart Busch, Johann Götz, Judith S. Heinisch, Klaus David

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## To cite this version:

Lars Mathuseck, Lennart Busch, Johann Götz, Judith S. Heinisch, Klaus David. UWBike: Global Bicycle Positioning System Using a Single Ultra-Wideband Reference Point. 2025. hal-05250591

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# UWBike: Global Bicycle Positioning System Using a Single Ultra-Wideband Reference Point

Lars Mathuseck

lars.mathuseck@uni-kassel.de

Chair for Communication

Technology, University of Kassel

Kassel, Germany

Lennart Busch

lennart.busch@uni-kassel.de

Chair for Communication

Technology, University of Kassel

Kassel, Germany

Johann Götz

johann.goetz@uni-kassel.de

Chair for Communication

Technology, University of Kassel

Kassel, Germany

Judith S. Heinisch

judith.heinisch@uni-kassel.de

Chair for Communication

Technology, University of Kassel

Kassel, Germany

Klaus David

david@uni-kassel.de

Chair for Communication

Technology, University of Kassel

Kassel, Germany

## Abstract

Cooperative collision avoidance systems promise collision prevention by exchanging movement information between road users to alert them of imminent collisions. Precise global bicycle positioning is one key requirement in these systems. Existing technologies, such as GNSS, fall short of consistently delivering the required positioning accuracy in the real world. Therefore, this paper presents *UWBike*, a novel ubiquitous precise bicycle positioning system based on smartphone sensors that allows accurate and low-cost global positioning and position tracking. *UWBike* uses the smartphone's camera in combination with visual-inertial odometry (VIO) for relative position tracking. We show that *UWBike* is a feasible solution for short-term dead reckoning, suitable for the dynamic motion of bicycles, achieving an error rate of 1.3 cm/m in real-world experiments. To achieve a global position, we introduce a novel algorithm to convert the local coordinate system, derived from relative position tracking, into a global reference frame, taking advantage of a single ultra-wideband reference point. Our results demonstrate high accuracy, with a mean position error of only 36 cm, validating the real-life feasibility of our system for cooperative collision avoidance systems.

## CCS Concepts

* Computer systems organization → *Embedded and cyber-physical systems*; 
* Computing methodologies → *Neural networks*; 
* Human-centered computing → *Ubiquitous and mobile devices*; *Ubiquitous computing*; *Ubiquitous and mobile computing systems and tools*.

## Keywords

Bicycle positioning; UWB; LiDAR; Camera; VIO; Bicycle Safety; Cooperative Collision

## 1 Introduction

As cycling gains popularity as a sustainable mode of transport, ensuring the safety of cyclists has become increasingly important. Traffic accidents involving bicycles and cars remain a serious concern, especially in urban areas [22]. One promising approach to

reducing these accidents is the use of cooperative collision avoidance systems [21].

Cooperative collision avoidance systems can initiate countermeasures when an imminent collision between a vehicle and a cyclist is predicted. Such countermeasures can be, for example, a collision avoidance maneuver of a car or a warning sent to the cyclist. While this vision for the future of intelligent transportation is appealing, one of the major challenges of collision avoidance systems is the requirement of high position accuracy of road users to avoid false alarms, and more importantly, to not miss imminent collision warnings (missed alarms). Concurrently, a bicycle warning system must be compact, lightweight, energy efficient, economically viable, and accurate [12]. These requirements make it very challenging to build a viable real-world solution.

A lightweight system that almost every cyclist brings with them is a smartphone. It can be considered a promising device for such a system, as the smartphone comes with onboard sensors, processing power, and energy supply. Furthermore, using a smartphone obviates the requirements for additional hardware, making such a system accessible, scalable, and affordable. However, today's smartphone GNSS, with a typical position precision of 2-4 meters [4, 24], lacks the required position accuracy of advanced collision avoidance systems. Furthermore, the sampling rate is limited to 1Hz, which is equivalent to a position update every ≈ 4 m when riding at a typical cycling speed of ≈ 15 km/h. Therefore, it is necessary to develop a novel method for bicycle positioning that surpasses the limitations of GNSS, offers improved positional accuracy, and supports a high sampling rate.

Consequently, this paper presents *UWBike*, a novel smartphone-based ubiquitous bicycle positioning system. We split the problem of global positioning into two parts: Relative positioning and mapping of this relative position to a global reference frame.

While developing *UWBike*, we encountered several challenges. First, accurate relative position tracking of a bicycle is challenging due to its high speed and maneuverability, combined with continuous balancing actions, which involve frequent small steering and leaning movements. The intricate nature of riding dynamics and common sensor data drift complicates relative positioning, making sophisticated sensor fusion techniques necessary.
The second challenge we encountered is how to convert the local coordinates from the relative position tracking to a globally shared coordinate system so that a cooperative collision avoidance system can use the position information to warn other road users.

*UWBike* addressed the first challenge by using a smartphone that is attached to the handlebar of the bicycle. Utilizing the smartphone's camera alongside the IMU, and, when possible, the LiDAR sensor, enables monitoring of relative position shifts over time through Visual Inertial Odometry (VIO) frameworks. These frameworks, such as Apple's ARKit or Google's ARCore [2, 10], are already used today for Augmented Reality and are known to work well for short-term relative position tracking during walking. However, it is unclear whether these frameworks are feasible for bicycle localization.

The second challenge is addressed using an ultra-wideband (UWB) reference point with known global coordinates. Using UWB allows for measuring the distance from the reference point to the smartphone attached to the bicycle. This reference point can be installed in smart infrastructure near dangerous intersections to protect cyclists in this area. Instead of relying on three or more reference points for triangulation, *UWBike* uses the relative position tracking to reduce the number of required reference points to a single point, which reduces installation and maintenance costs.

We performed two different measurements in real-world scenarios to evaluate the accuracy of *UWBike*. First, we evaluated the accuracy of the relative position tracking by comparing it to precisely measured control points. Second, we installed a UWB sender at a known global reference point and compared the global coordinates calculated by *UWBike* with the ground truth. Both experiments were performed in the real-world and with a instrumented bicycle to demonstrate the feasibility of our system.

In summary, this paper makes the following contributions:

* We propose *UWBike*, the first smartphone-based bicycle positioning system to enable accurate, low-cost global positioning. Our results demonstrate an accuracy of 36 cm for global positioning.

* Using visual-inertial odometry (VIO) from the smartphone camera (and optional LiDAR), we present a lightweight and feasible solution for short-term dead reckoning, and we show that it is suitable for the dynamic motion of bicycles.

* We introduce a novel global positioning algorithm that enables accurate localization using a single UWB reference point, in contrast to traditional UWB systems that require three or more reference points.

# 2 System Design

Our global positioning approach combines relative positioning with UWB to map relative position to a global reference frame. The approach uses a single UWB reference point that is tracked over time for triangulation. Hence, an accurate relative position is essential for the efficacy of the triangulation process.

Consequently, we first introduce and evaluate the relative positioning component. With this component in hand, we propose and evaluate a novel global positioning algorithm, applying a single UWB reference point.

<figure><img src="image_3.png" /><figcaption>**Figure 1:** Our instrumented bicycle, equipped with two smartphones and a Real-Time Kinematic positioning (RTK) device. For our approach, only one of the smartphones would be required.</figcaption></figure>

A precise ground truth of the current position of the bicycle is needed to evaluate the accuracy of our approach. We used an A08-RTK GEO-MICHEL [1], a professional-grade real-time kinematics enabled GNSS receiver. In a preliminary test, we validated the accuracy of the Real-Time Kinematic positioning (RTK) device by placing it on known reference points from the local surveyor's office. This RTK device has a precision of 0.01 m while stationary.

## 2.1 Relative Positioning

In relative positioning, the position of the bicycle is tracked over time from a starting point in a local reference frame. In contrast to a global reference frame, which is shared across road users, the local reference frame is only known to the bicycle's smartphone.

We used Visual Inertial Odometry (VIO) for relative positioning, because it has a high sampling rate (i.e., 60 Hz), is available on most modern smartphones and is known for its high relative position accuracy in Augmented Reality applications [6].

The VIO frameworks from Apple and Google utilize motion information from IMU sensors and combine it with computer vision. ARKit and ARCore extract natural features from the image, such as edges, and use them as reference points. This image information can be fused with depth information from, for example, the solid-state LiDAR of the Apple iPhone. The position difference of the reference points in consecutive images is merged with IMU data to calculate the movement of the device. The relative position is then calculated for each recorded camera frame. We performed experiments to examine the accuracy of positioning a bicycle in a relative coordinate system using the VIO implementation of ARKit/ARCore. As VIO frameworks are primarily designed for indoor, human walking-speed, hand-held applications, their performance in bicycle-mounted scenarios remains an open question. Given the promising potential of VIO in cycling applications, this study will evaluate its accuracy in such applications.

### 2.1.1 *Experiment setup.* We used an instrumented bicycle to perform experiments in the real world. The instrumented bicycle, as shown in Fig. 1, is equipped with our precise RTK device and two
<figure><img src="image_4.png" /><figcaption>Figure 2: The test track for evaluation of the relative positioning</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 3: Relative positioning errors for segments of the test track between 20 m and 100 m long. (Outliers are not shown for readability)</figcaption></figure>

smartphones. We attached two smartphones to evaluate the different frameworks. For our global positioning approach and a real application, only one smartphone is necessary. All devices are mounted on the handlebar, the RTK device is placed directly in the center, and the smartphone cameras face the riding direction. This placement is a common mounting point for smartphones and bicycle computers. The smartphones we used are a Google Pixel 7 Pro and an Apple iPhone 15 Pro. We recorded the data from ARKit and ARCore on smartphones for subsequent offline analysis. The position data from the RTK device is used as ground truth.

We have ridden our instrumented bicycles 20 times on a track that is 725 m long (see Fig. 2). We stopped the bicycle roughly every 20 meters while riding the test track to create control points for later evaluation.

### 2.1.2 Evaluation.
The local coordinate systems of the smartphones were converted into a global scope to compare the relative position of the smartphones to the precise global position of the RTK device. This was done by using the first 10 m of the track in both the local positioning and RTK data. We mapped the axes of the local coordinate system to north and east so that both tracks overlap in a global reference frame.

For each control point, we define a segment as the next 20, 40, 60, 80, and 100 m of the track. For each segment, we used the last position from the VIO framework and RTK and measured the relative position error using the Euclidean distance. In total, we evaluated a distance of ≈90 km.

Fig. 3 shows the box plot that represents the median position error over all segments. It can be seen that the median error and the interquartile range slowly increase with the distance ridden, whereas the lower whisker shows that a minimal error is still possible. Furthermore, we observed that Apple's ARKit median error is outperforming Google's ARCore for every controlled distance. This may be due to the fact that the iPhone uses the built-in LiDAR sensor as an additional sensor, which could be a reason for the higher position accuracy. Our findings indicate that on ARKit, the average error is 1.3 cm/m and for ARCore, the error is slightly higher with 2 cm/m. Our data show that within a distance of 100 m, only 25% of the segments have an error above 1.45 m for iOS and 1.74 m for Android. We argue that both approaches are promising for short-distance bicycle positioning, because the position error significantly outperforms GNSS.

## 2.2 Global positioning

With relative positioning in hand, we now introduce our novel global positioning algorithm for bicycles.

UWBike uses a single reference point with known global coordinates in combination with ultra-wideband (UWB) for global positioning. UWB allows centimeter-level distance measurements between such a UWB reference point (sender) and multiple receivers [14]. An increasing number of smartphones are equipped with UWB transceivers, which enable high-precision distance measurements from the UWB reference point (sender) to the smartphone attached to the bicycle (receiver). With simple triangulation, the position could already be determined. However, triangulation requires at least three UWB reference points [8, 17], which is a hard constraint in the real world, given the limited range of UWB. To overcome this limitation of existing UWB-based positioning solutions, we propose using only a single UWB reference point, minimizing installation and maintenance costs. Such a reference point can be installed on smart infrastructures (e.g., traffic lights or street lights) with readily available positions, as provided in digital twins.

### 2.2.1 UWB-based positioning algorithm.
The idea of our algorithm is to measure multiple distances to the same single UWB over time with the changing position of the bicycle, as shown in Fig. 4.

Each distance measurement defines a circle around the bicycle, with the measured distance as the radius and the current position of the bicycle as the center. To locate the UWB reference point, multiple distance measurements are recorded while the bicycle moves toward it (Fig. 4a). For each measurement, the relative position
<figure><img src="image_6.png" /><figcaption>Figure 4: Proposed approach for mapping the relative coordinates to a global reference frame. (a) Ultra-wideband (UWB) distance measurements to UWB reference point. (b) Clustering of distance measurements' intersections and cluster selection. (c) Density center calculation and distance error calculation.</figcaption></figure>

of the bicycle, the timestamp, and the last known GNSS position of the smartphone are recorded. (Note: GNSS from smartphones is not used for positioning, because of the previously discussed inaccuracies, but is required later in the algorithm.)

Once the measured distance to the reference point stops decreasing, the measurement process is stopped. At this point, all circles obtained from the distance measurements can be analyzed. The intersection points between the smallest (i.e., closest) circle and the other circles are calculated. Due to small UWB inaccuracies, these intersection points form two spatial clusters. The *real location* of the reference point is expected to lie near one of the cluster centers (Fig. 4b).

K-Means clustering with $K = 2$ is applied to identify the centroids of the two clusters. These centroids are candidates for the real position of the UWB reference point in relative coordinates. Since centroids can be affected by outliers, a refinement step is performed: A grid search is applied within a radius of 20 cm to the intersection points in the selected cluster (Fig. 4c). We tested different parameters for the radius and found good results with 20 cm. The point with the highest local density is then taken as the most likely position of the reference point.

To determine which of the two cluster centroids is *correct*, we use additional information from the recorded (albeit imprecise) smartphone GNSS or map data. Although GNSS is not submeter accurate, it is sufficiently precise to identify which centroid is more plausible, based on its proximity to the recorded GNSS location or the direction of travel in combination with map data.

Once the centroid is chosen, we can establish a transformation from the relative coordinate system to a global coordinate system, because the global position and the relative position of the reference point are known.

The pseudocode for this method is provided in Algorithm 1

<figure><img src="image_7.png" /><figcaption>Figure 5: Experiment setup of the global positioning experiment (birds-eye-view).</figcaption></figure>

2.2.2 *Experiment setup.* First, we validated the UWB distance measured between the sender and receiver under ideal conditions by placing the sender and receiver next to each other. We recorded an accuracy of approximately ±2 cm. In addition, we conducted a second real-world experiment to evaluate the proposed algorithm. For this experiment, our instrumented bicycle was ridden along a straight path of 40 m in a parking lot at an average riding speed of about 15 km/h. The instrumented bicycle was equipped similarly to the previous experiment in Fig. 1, but without the Android device, as the relative position on iOS was found to be more precise. The setup of the test environment is shown in Fig. 5. We simulated a UWB reference point by placing a UWB sender next to the path. Similarly to a traffic light in a real-world scenario, the UWB reference point was placed at an elevated position. This point was measured using our RTK device. At the end of the ride, the user stopped and stayed stationary to receive a precise location from the RTK device. This process was repeated 50 times.

2.2.3 *Evaluation.* At the control point at the end of the test track, we compared the final position measured by both the RTK device and our algorithm-derived position and assessed the positioning
**Algorithm 1:** Global Positioning using a Single UWB Reference Point

**Input:** Time series X with relative coordinates, GNSS coordinates, and distances to the UWB beacon

**1. Filter Time Series Data:**

Traverse X and examine the distances;

Stop when the current distance is greater than the previous;

Copy all entries up to this point into a new dataset X';

**2. Determine Intersection Points:**

foreach entry in X' do

Calculate a circle around the relative coordinate with a radius equal to the distance;

Calculate intersection points between the smallest circle and all other circles;

**3. Apply K-Means Clustering:**

Let x' be the last entry of X';

Cluster the intersection points into 2 clusters using K-Means;

Compute vectors between x' and the 2 centroids;

Using the GNSS coordinates of x' and the vectors, compute GNSS positions of the centroids;

Let Y be the cluster whose centroid is closest to the UWB beacon in GNSS coordinates;

**4. Find Density Center:**

foreach point y ∈ Y do

Count the number of neighbors within 20 cm

Euclidean distance;

Let Z be the point with the most neighbors;

**5. Calculate Global Position:**

Use the vector from x' to Z to compute the precise global GNSS position;

error through the Euclidean distance. In the best case, the global positioning error is 10 cm, while in the worst case it reaches 69 cm. An analysis of the results can be found in Tab. 1. There are multiple factors that contribute to this error. First, there is a distance error of ±2 cm from UWB. Second, the algorithm and, therefore, the last position are based on the measured relative position of the instrumented bicycle, which itself is error-prone. Overall, our findings demonstrated a mean positional error of 36 cm. This surpasses smartphone GNSS performance and presents a promising approach to enhancing the accuracy of bicycle positioning in practice.

<table><caption>**Table 1:** Results of the global positioning in centimeters.</caption><thead><tr><td>Statistic</td><td>Error</td><td>Statistic</td><td>Error</td></tr></thead><tbody><tr><td>Average / Mean</td><td>36 cm</td><td>Maximum</td><td>69 cm</td></tr><tr><td>Mode</td><td>37 cm</td><td>Minimum</td><td>10 cm</td></tr><tr><td>Median</td><td>36 cm</td><td>Standard Deviation</td><td>13 cm</td></tr></tbody></table>

**3 Discussion**

As shown in section 2.2.3, the global positioning has an average error of 36 cm. However, this error contains multiple sources of

error: the UWB distance measurement error, the selection of the correct cluster and centroid, and the relative position tracking. The UWB distance measurement error was ±2 cm. Thus, the effect of this error is small and negligible. Although in our experiments, the correct cluster centroid was always selected, the selection depends on the accuracy of the GNSS precision. To further improve robust cluster selection, multiple past GNSS locations could be used to determine the riding direction, and thus, in combination with map information, filter out implausible selections. The relative positioning shows an error rate of 1.3 cm/m. Therefore, the main influence of the global position error is the relative position tracking. With improved VIO algorithms, this error could further decrease in the future.

Furthermore, when the cyclist's global location is established using a UWB reference point, the position error grows with the distance. To continuously ensure precise cyclist positioning, it would be necessary to install a new reference point along bicycle routes. Therefore, these reference points must be strategically distributed throughout the city, starting at dangerous intersections and along bicycle routes, to avoid accidents in these areas. In later stages, such a system can be rolled out city-wide.

Users must attach their smartphone to the handlebar to use UWBike. If users carry the smartphone in their pocket or in a bag, our camera-based approach is not suitable. However, we believe that the benefit of road safety is of such importance that many users would be willing to attach their smartphone to the handlebar.

**4 Related Work**

**Satellite-based:** GNSS is the obvious approach for global positioning, but it comes with limitations such as a low sampling rate of 1 Hz, multipath effects in urban canyons, it is prone to jamming, and the position accuracy is limited to around 2-4 meters for smartphone-based GNSS [4, 24]. These limitations render it insufficient for accurate bicycle positioning. The expensive RTK-GNSS can overcome some of these limitations, but it is not economically feasible due to the high costs. As stated by multiple authors, a position accuracy of <1 m of VRUs is required for the precise detection of upcoming collisions with cars in cooperative collision avoidance systems [3, 15]. With today's smartphones, this position accuracy has not yet been achieved.

**IMU-based:** Cyclist movement data from IMUs can serve as input for dead reckoning algorithms.

Shebl *et al.* [19] used a proximity sensor near a wheel to estimate the distance traveled based on the wheel rotation.

IMU-based approaches still require a global reference point, such as GNSS. Thus, the positioning accuracy is influenced by the accuracy of the global reference. Furthermore, IMUs are known to be prone to sensor drift over time.

**Vision-based:** The approach proposed by Yadav *et al.* [23] uses a rear-facing camera on the bicycle to position the cyclist within bicycle swarms using object detection and monocular depth estimation. Another approach for relative positioning is to use a stationary LiDAR, as proposed by Lee *et al.* [13]. They placed a rotating LiDAR at the experiment site and used the point-cloud data to reconstruct the trajectory information. Stasinopoulos *et al.* [20] pioneered the
use of LiDAR for Simultaneous Localization and Mapping (SLAM) in the context of autonomous bicycles.

However, such a large, expensive, and energy-demanding LiDAR is not feasible for a bicycle in daily use. In contrast, we propose employing a lightweight, low-energy, solid-state LiDAR sensor. This configuration presents a compact, cost-effective, and therefore feasible solution for incorporation into a bicycle.

**Sensor Fusion:** Sensor fusion utilizes multiple data sources from different sensors. Diaz *et al.* [9] combine GNSS with IMUs and magnetometers from different wearable sensors mounted at different locations on the human body. In addition, Chang *et al.* [5] utilizes the detected pedaling cycles and frequency from the IMU, magnetometers and barometers based on the current gear to estimate the cyclist's position during GNSS outages.

Reitberger *et al.* [18] used wide-angle cameras at intersections for triangulation and fused this data with smartphone IMU to provide global position of the cyclist. Miah *et al.* [16] fused IMU, absolute optical encoder and Hall Effect sensors for the positioning of the bicycle.

While sensor fusion is a promising approach in terms of improving the positioning accuracy, it requires additional sensors and processing power on the bicycle.

**Ultra-wideband:** Positioning can also be achieved using ultra-wideband (UWB) reference points installed on infrastructure to enable precise triangulation.

[8, 17] of the cyclist's position, directly attached to the cyclist or the car [7, 8, 11, 17, 25].

UWB enables distance measurements with centimeter precision, making it a valuable technique for positioning. Although these systems allow for precise positioning, they require multiple reference points within an area, which can become costly if they are scaled to a city. Although our approach uses a similar method, it only requires a single UWB reference point for positioning within an area, helping to reduce installation and maintenance costs.

## 5 Conclusion

This paper presents *UWBike*, a novel ubiquitous smartphone-based bicycle positioning system. UWBike uses Visual Inertial Odometry (VIO) based on smartphone sensors for relative position tracking. We show in real-world experiments that such an approach can be used to track the bicycle position with a position error as low as 1.3 cm/m. We use this relative position tracking in combination with ultra-wideband for global positioning. In contrast to existing approaches that rely on three or more reference points, UWBike requires only a single reference point, minimizing installation and maintenance costs. Overall, UWBike achieves 36 cm accuracy for global positioning. The results demonstrate that UWBike outperforms smartphone GNSS and is feasible for real-world applications, such as cooperative collision avoidance systems.

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Received 13 June 2025; accepted 22 July 2025