# Assessing the 6-DoF Accuracy and Robustness of the HTC VIVE Ultimate ® Stand-Alone Inside-Out Motion Tracker: A Robot-Driven Evaluation

Julian Kulozik, Nathanaël Jarrassé

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Julian Kulozik, Nathanaël Jarrassé. Assessing the 6-DoF Accuracy and Robustness of the HTC VIVE Ultimate ® Stand-Alone Inside-Out Motion Tracker: A Robot-Driven Evaluation. IEEE Sensors Journal, inPress, 10.1109/JSEN.2026.3657180. hal-05496408

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# Assessing the 6-DoF Accuracy and Robustness of the HTC VIVE Ultimate® Stand-Alone Inside-Out Motion Tracker: A Robot-Driven Evaluation

Julian Kulozik<fnref n="1" />, Nathanaël Jarrassé<fnref n="1" />

**Abstract**— The HTC VIVE Ultimate Tracker®, using inside-out tracking with internal stereo cameras for 6 DoF tracking without external cameras, offers a cost-efficient and simple setup for motion tracking. Originally for the gaming and VR industry, we explored its application beyond VR, providing C++ and Python source code for data capturing without a VR headset. This is the first study to evaluate the tracker's accuracy in various experimental scenarios, including robotic and natural human movements. To assess the robustness of the displacement tracking accuracy, we used a robotic arm for precise, repeatable motion. Using an optical motion capture system as a reference, we conducted tests under varying conditions: lighting, movement velocity, environmental changes from displacing objects and human movement in front of the trackers, and varying the range of motion. On average, the HTC VIVE Ultimate Tracker showed an accuracy of 4.98 mm ± 4 mm. In stark contrast, orientation accuracy was highly robust and consistent across all conditions, with a mean RMSE of 0.36° ± 0.31°, and remained unaffected by the perturbations that degraded positional tracking. Key factors influencing accuracy were lighting and range of motion. Under optimal conditions—good lighting and a reduced range of motion—the accuracy and precision improved, resulting in a mean error of 2.59 mm with a standard deviation of ± 0.81 mm. We also used five trackers to record human whole-body movements in ecologically valid scenarios, demonstrating an approximate positional accuracy of 1 cm, validated through two distinct pick-and-place tasks. While less accurate and precise than traditional fixed-camera-based motion capture systems, the HTC VIVE Ultimate Tracker® is a compelling alternative for research due to its low cost, ease of use, and capacity to track human and object motion beyond the constraints of conventional VR or motion capture environments.

**Index Terms**— otionotionM capture, Pose estimation, Performance evaluation, Motion analysis, Sensors, Error analysis

Robotic displacement
as reliable/repeatable source of motion

<figure><img src="image_2.png" /></figure>

Assessing the tracking precision of the
VIVE Ultimate Tracker®

<figure><img src="image_3.png" /></figure>

- Inside-out tracker

- library for stand-alone use

<figure><img src="image_4.png" /></figure>

OptiTrack® system as ground truth measurement

Tracking body movement
with 5 Vive Ultimate Trackers ○

low/medium speed pick and place

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

high dynamic sport movement

<figure><img src="image_7.png" /></figure>

## I. INTRODUCTION

MOTION capture systems play a critical role in various applications, ranging from virtual reality (VR) and gaming to biomechanics, robotics, and human-computer interaction [1]. Traditional motion capture systems, such as Vicon® or OptiTrack®, which use camera-based setups with reflective markers, are both renowned and well documented for their high accuracy, precision and reliability [2], [3], [4]. However, these systems require extensive setup, involving multiple external cameras and complex calibration procedures. They also demand large spaces to provide an adequate field

of view, making deployment challenging in smaller or constrained environments.

Advancements in consumer-grade tracking technologies, like the VIVE Ultimate Tracker released in November 2023, offer more accessible and cost-effective alternatives. The VIVE Tracker uses inside-out tracking with embedded stereo cameras and SLAM (Simultaneous Localization and Mapping) algorithms, operating independently of external cameras, which significantly reduces the cost and complexity of setup compared to traditional systems. The decision to focus exclusively on this device is motivated by its unique characteristics as a fully self-contained, battery-powered, and wireless tracker that performs all computations on-board. Unlike component-level trackers (e.g., Intel Realsense®) that require a host computer, its stand-alone form factor makes it uniquely suited for the untethered human motion analysis that is the targeted application of this work.

<footnote n="1">¹Sorbonne Université, CNRS, INSERM, Institute for Intelligent Systems and Robotics (ISIR), Paris, France.</footnote>

<footnote>This research was partly funded through the Agence Nationale de la Recherche support, under grant #ANR-22-EXOD-0002. Email: kulozik@isir.upmc.fr</footnote>
Although much of current research focuses on markerless motion capture solutions, especially in biomechanical applications [5], [6], [7], these methods still depend on external cameras, leading to similar challenges in terms of space and setup complexity. Previous studies have also evaluated the HTC VIVE Tracker 3.0, which uses outside-in tracking technology that relies on external base stations for positioning, showing promising results in comparison to Vicon systems [4]. Indeed, a significant body of work has characterized these earlier base station-based Vive systems, establishing their sub-millimeter precision while noting challenges with accuracy [8], developing methods to improve performance for robotics [9], evaluating them for applications like indoor localization [10], [11], and creating advanced sensor fusion algorithms to enhance dynamic tracking [12]. However, these findings are specific to outside-in laser tracking and cannot be generalized to the VIVE Ultimate Tracker, which relies on computer vision and on-board sensor fusion. This fundamental hardware shift necessitates a new, dedicated evaluation protocol to understand the specific error modalities—such as scale drift and lighting sensitivity—inherent to inside-out SLAM architectures.

While the VIVE Ultimate Tracker relies on inside-out tracking, it differs from classical Visual-Inertial Odometry (VIO) systems. VIO estimates motion by integrating inertial measurements with visual feedback but does not maintain a persistent map of the environment. In contrast, the VIVE Ultimate Tracker localizes itself within a pre-mapped environment, which may be continuously updated. This distinction makes it conceptually different from traditional VIO approaches, positioning it somewhere between VIO and SLAM-based tracking.

The field of Visual-Inertial Odometry has extensively explored techniques for fusing IMU data with visual features to achieve robust 6-DoF pose estimation in real time [13], [14], [15]. Consumer-grade VIO-based trackers, such as the Stereolabs ZED 2/2i, provide motion tracking at a pose update rate of 100 Hz and have a reported pose drift of 0.35% relative to the total displacement [16]. However, this level of accuracy is typically achieved for large displacements spanning several meters, whereas applications like accurate body motion tracking, which involve smaller-scale movements, may exhibit different error characteristics.

A study comparing four off-the-shelf VIO trackers [17] qualitatively assessed their tracking performance. Based on large-scale experiments, ARKit was selected as the most suitable candidate for a quantitative evaluation using OptiTrack as a ground truth reference in a room-scale experiment. In this study, an iPhone 12 Pro Max running ARKit was mounted on a chair and pushed along predefined trajectories, resulting in a relative pose error (RPE) [18] between 2 and 4 mm/s. The final drift errors for a 2 m range of motion for different trajectories varied significantly, from 20 mm to 260 mm.

Unlike traditional Visual-Inertial Odometry (VIO) trackers, the VIVE Ultimate Tracker is specifically designed for accurate and precise body motion tracking within a confined space. It enables the simultaneous use of multiple trackers in a single, pre-mapped environment with a shared coordinate system [19]. This setup is ideal for applications requiring

detailed tracking of multiple objects or body parts in a structured environment. These differences compared to large-scale tracking applications highlight that the VIVE tracker cannot be directly assessed with classical VIO evaluation methodologies.

The primary objective of this study is to evaluate the measurement accuracy of the VIVE Ultimate Tracker and assess its feasibility for human motion tracking beyond its intended VR gaming use case. To this aim, we measure its accuracy using a standard optical motion capture system, OptiTrack. The main contributions of this work are summarized as follows:

1) **Methodological Framework:** We establish a reproducible, robot-driven protocol for characterizing the accuracy, latency, and robustness of stand-alone inside-out trackers.

2) **Device Characterization:** We identify specific error modes of the VIVE Ultimate Tracker, distinguishing between general SLAM limitations (e.g., lighting sensitivity) and device-specific behaviors (e.g., anisotropic scaling errors).

3) **Application Validation:** We validate the feasibility of using this stand-alone system for untethered human motion analysis in ecologically valid scenarios.

## II. MATERIALS AND METHODS

### A. Experimental Setup

a) **Robotic movement:** For the first experimental setup, shown in Fig. 1, we employed a KUKA LWR4+ 7 DoF robotic arm in combination with a 5-camera OptiTrack system. The decision to use the OptiTrack system, rather than relying solely on the robot's direct kinematics, was made to ensure that the data collected would be accurate and consistent, allowing for reliable comparisons between robotic and human movement experiments.

The OptiTrack cameras, including two PrimeX 13W and three Prime13, offer a 3D accuracy ranging from ±0.20 mm to ±0.30 mm and were configured to capture data at a frame rate of 240 FPS. The PrimeX 13W cameras provide a tracking range of up to 9 meters for passive markers. In contrast, the Prime13 cameras have a tracking range of up to 16 meters [2]. While manufacturer specifications conservatively cite a rotational accuracy of < 0.5° for individual cameras, empirical validations of the OptiTrack system in multi-camera rigid-body configurations demonstrate substantially higher precision. Experimental evaluations of the Motive® solver report rotational accuracies typically on the order of ± 0.1°. [3] In our setup, the cameras were mounted on the ceiling, positioned in different directions approximately 3 to 4 meters from the robot's end-effector. It is important to note that the OptiTrack system served as the sole ground truth reference. The robot was employed exclusively to generate repeatable trajectories; therefore, the internal positioning accuracy of the robot itself does not influence the error calculations, as the comparison is strictly between the VIVE Tracker and the OptiTrack system.

b) **Pick-and-place task:** In a human pick-and-place task, we assessed the accuracy of multiple trackers. First, the subject was instructed to pick up cups and place them at varying distances while standing. The cups were positioned at increasing
distances as shown in Fig. 2 on the left. The range of motion (ROM) of the movement in this case is approximately 0.7 meters along the x-axis, 0.15 meters along the y-axis, and 0.1 meters along the z-axis. In the second experiment, we increased the task's range of motion by positioning the goal destinations at different distances and heights as shown in Fig. 2 on the right. This required the subject to walk forward and backward to reach the targets. This introduced a broader range of motion, with the dynamic case ROM extending to 1.5 meters along the x-axis, 0.7 meters along the y-axis, and 0.7 meters along the z-axis. Trackers were affixed to key points on the subject's body, including the wrist, elbow, upper arm, hip, and knee. For ground-truth comparison, OptiTrack marker sets were rigidly affixed to each VIVE tracker.

c) **Fencing**: In our final experimental setup shown in Fig. 3 and inspired by the pioneering work of Etienne-Jules Marey from 1890 [20], we challenged an elite fencer to perform repetitive lunges while tracking key points on the hip, knee, shoulder, wrist, and elbow, with both VIVE trackers and OptiTrack markers.

<figure><img src="image_8.png" /><figcaption>Fig. 1. Experimental Setup KUKA robot. (a) OptiTrack cameras attached to the ceiling facing the robot, (b) KUKA LWR 4+ robot, (c) VIVE Ultimate Tracker, (d) OptiTrack marker set attached to the VIVE tracker, (e) Computer capturing OptiTrack data, (f) computer capturing VIVE data and receiving data from (e), second OptiTrack-VIVE marker set, attached to the robot's movable base. (g)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 2. Experimental setup for two pick-and-place tasks using 5 attached trackers with each one OptiTrack marker set (1): knee, hip, shoulder, elbow, and wrist. Left panel: task performed while standing still. Right panel: task performed with walking. The goal destinations are marked by (A) and (B) in the panels.</figcaption></figure>

The participant provided written informed consent prior to testing. The human motion experiments were designed as device stress tests to evaluate the tracker's ability to maintain continuous tracking during complex, ecologically valid movements. It is important to note that the VIVE Ultimate Tracker is a consumer device engineered for broad physical wearability. Furthermore, as an independent 6-DoF sensor, its internal tracking accuracy is determined by kinematic

<figure><img src="image_10.png" /><figcaption>Fig. 3. Experimental setup for the fencing task with trackers attached to the knee, hip, shoulder, elbow, and wrist.</figcaption></figure>

constraints (velocity, acceleration) rather than anthropometric variables. Consequently, a single subject was deemed sufficient to generate the necessary kinematic diversity to validate the sensor's performance, as expanding the participant pool would primarily assess mounting stability rather than intrinsic tracking accuracy.

d) **Data Acquisition Hardware**: Data capture was performed on a laptop (Intel Core i9-13950HX, 32GB RAM, NVIDIA RTX 4090 Laptop GPU, Windows 11) using VIVE Hub 2.3.3b BETA. Custom Python scripts [21] were used to interface with the trackers. As the tracker performs all SLAM computations on-board, these host specifications affect only data logging integrity, not the internal tracking performance.

## B. Procedure

a) **Robotic movement**: To ensure a reproducible setup, we defined four distinct movement patterns for the robot, as shown in Fig. 4. These patterns were selected to cover the fundamental kinematic primitives of motion: the 'Circle' represents continuous curvilinear motion with constant velocity, while the 'Cube' represents linear motion with discontinuous directional changes and variable acceleration profiles. These included a cube and a circle with dimensions of approximately 5 cm (A), 10 cm (B), and 20 cm (C). The largest pattern (D) spans about 80 cm x 40 cm x 20 cm, which is near the robot's workspace limits, with a circle diameter of 40 cm.

Since the robot served solely as a source of reproducible movement, perfect execution of cube or circular patterns was not required. We opted for joint control to maximize the robot's dynamic capabilities. By controlling the redundant 7-DoF robot in this manner, we could more easily exhibit the its dynamic capabilities.

The velocity limits for each movement pattern (A, B, C, and D) varied based on displacement size and dynamic constraints. Pattern A involved a 5 cm displacement, Pattern B a 10 cm displacement, Pattern C a 20 cm displacement, and Pattern D an 80x40x20 cm cube paired with a 40 cm circle. For the cube movements, the peak velocities were 0.2 m/s, 0.8 m/s, and 0.4 m/s for Patterns A, B, and C/D, respectively. The average velocity for each cube was half of the peak velocity due to acceleration and deceleration phases. The rotational range of motion for these patterns reached a maximum of approximately 25°.
<figure><img src="image_11.png" /><figcaption>Fig. 4. Four pre-programmed robotic movement patterns. (A) 5cm, (B) 10 cm, (C) 20 cm, (D) 80 x 40 x 20 cm cube + 40 cm circle</figcaption></figure>

For the corresponding circular movements, the same average velocity achieved in the cube patterns was used as a constant velocity. Thus, the circular movements in Patterns A, B, and C/D were performed at 0.1 m/s, 0.4 m/s, and 0.2 m/s, respectively.

Smaller displacements were limited by jerk due to short distances, while larger displacements were constrained by the robot's torque capacity where the weight further from the center required more torque.

The experimental setup involved varying lighting conditions using both the room's ceiling lights mounted at a height of 2.8m and an additional dimmable LED light source around 3 m away from the robot shining at the scene. The ceiling lights provided general illumination across the entire room, while the LED light source offered adjustable lighting. These lights were positioned to allow for controlled changes in the room's overall brightness. The LED light was capable of being dimmed from a very low brightness level, creating a dim environment, to full brightness, simulating more intense lighting conditions. This setup enabled us to simulate a range of lighting scenarios from well-lit to low-light environments.

The experimental setup was also designed to evaluate the impact of environmental perturbations on the VIVE Ultimate tracker's accuracy. The setup included movable objects, such as chairs and various items, that were strategically positioned around the tracking area. These objects could be rearranged during the experiment to simulate changes in the environment. Additionally, the setup allowed for human activity in front of the tracker's built-in cameras.

b) **Pick-and-place task:** One subject performed a pick-and-place task under two conditions. Each trial consisted of 5 complete pick-and-place cycles (picking up a cup from a starting position and placing it at a target destination). Three

<figure><img src="image_12.png" /><figcaption>Fig. 5. Calibration process. (1) Rotating tracker with different centers of rotation, (2) identifying center with smallest relative displacement measured, (3) adding OptiTrack markers and aligning OptiTrack center of created rigid body with identified VIVE center of measurement, (4) displacing tracker in a cube pattern around 5 cm in each direction, (5) calculating transformation between VIVE and OptiTrack frame, (6) transforming VIVE data and storing the transformation matrix.</figcaption></figure>

trials were performed. The subject was instructed to perform the movements at a natural, consistent pace.

c) **Fencing task:** A single subject performed three trials. Each trial consisted of eight lunges, performed at maximum speed. A one-minute rest period was provided between trials to prevent fatigue.

## C. Data and Statistical Analysis

a) **Calibration:** In our initial step, we calculated the transformation between the VIVE frame and the OptiTrack frame. To do this, we first needed to identify the VIVE tracker's physical center of measurement. Without specific information from the HTC VIVE support and development team, we determined this center experimentally.

A critical prerequisite for accurate calibration is the precise identification of the VIVE tracker's internal measurement center. This is the virtual point within the tracker's physical volume for which the device reports its position. To determine this point empirically, we devised a systematic procedure leveraging the KUKA robot's high-precision motion. We systematically tested a grid of candidate Tool Center Points
(TCPs), $\mathbf{p}_c$, defined in the robot's end-effector frame around a visually estimated center of the tracker (Fig. 5, Step 1).

For each candidate TCP, $\mathbf{p}_c^{(k)}$, the robot was programmed to execute a pure rotational movement (e.g., ± 90 degrees around its principle axes) while the tracker's 3D position, $\{\mathbf{x}_i^V\}^{(k)}$, was recorded. The ideal measurement center is the point that, when used as the center of rotation, produces the minimum possible motion in the tracker's own reported data. We therefore defined a cost function, $J$, representing the positional variance for a given candidate point:

$$J(\mathbf{p}_c^{(k)}) = \sum_{i=1}^{N} \left\| \mathbf{x}_i^V(\mathbf{k}) - \bar{\mathbf{x}}_V^{(k)} \right\|^2 \quad (1)$$

where $\bar{\mathbf{x}}_V^{(k)}$ is the mean of all position vectors recorded during the rotation for that specific TCP. The optimal measurement center, $\mathbf{p}_c^*$, was then identified as the candidate TCP that minimized this cost function:

$$\mathbf{p}_c^* = \arg \min_{\mathbf{p}_c^{(k)}} J(\mathbf{p}_c^{(k)}) \quad (2)$$

This procedure identified a stable and repeatable point located near the geometric center of the tracker's base, which was then used for all subsequent calibration and measurement steps (Fig. 5, Step 2). This point is located near the center of the tracker's base, which we used for all subsequent calculations.

By adding reflective markers to the outside of the tracker a rigid body was created in the OptiTrack software. Its center was then manually aligned within the OptiTrack software in all three dimensions with the identified center of the VIVE frame (Fig. 5, Step 3). In the next step, the robot displaced the tracker in a cube pattern with a cube size of 5 cm and at a velocity of 0.025 m/s (Fig. 5, Step 4). The side length of 5 cm is large compared to the sub-millimeter noise level of both systems but small compared to the full workspace, so in this local neighborhood the mapping between the two frames can be safely approximated as rigid. The calibration thus defines a well-resolved local reference region around the origin from which distance- and direction-dependent distortions in the VIVE coordinate space can later be quantified. During this, we captured both the OptiTrack's and the VIVE's positional data. Each was in its own frame, which is shown in Fig. 5, step 5, where VIVE 1* is the VIVE tracker's position in the VIVE frame and OptiTrack 1 is the OptiTrack rigid body's position in the OptiTrack frame. To calculate the transformation between the two frames we used a commonly known method of centroids and Singular Value Decomposition (SVD) to both find the translation and the rotation between the two measurements [22].

Specifically, let $\{x_i^V\}$ and $\{x_i^O\}$ be paired points in the VIVE and OptiTrack frames with centroids $\mu_V$ and $\mu_O$. The cross-covariance is

$$H = \sum_{i=1}^{N} (x_i^V - \mu_V)(x_i^O - \mu_O)^{\top}. \quad (3)$$

The SVD of this matrix, $H = U\Sigma V^\top$, is then used to find the optimal rotation matrix $R_{OV}$ and the translation vector $t_{OV}$

that map the VIVE frame to the OptiTrack frame:

$$R_{OV} = V D U^\top, \quad t_{OV} = \mu_O - R_{OV} \mu_V, \quad (4)$$

where $D = \text{diag}(1, 1, \text{sign}(\det(VU^\top)))$ ensures $\det(R_{OV}) = +1$. This yields the proper rigid mapping (rotation + translation, without any scaling term) from VIVE to OptiTrack.

In Step 6 of Fig. 5, we can see an example of well-matched OptiTrack and VIVE positional data with a spatial distance of 0.80 mm ± 0.34 mm. The transformation matrix, defined as $\text{opti}T_{vive}$, is stored and used to convert the VIVE data to the OptiTrack frame. The calibration steps 4 to 6 were repeated before each set of experiments.

However, it is important to note that calibration results were not always consistent. In some cases, significant scaling issues led to larger measurement errors. When this occurred, we re-scanned the room according to VIVE software instructions and repeated the calibration process until the scaling issues were resolved. All experiments were conducted only when the calibration error was below 1mm, as illustrated in Fig. 5, Step 6. Once such a calibration had been obtained, the rigid transform was kept fixed for a given session, so that any distance- or direction-dependent errors observed in Section III-C reflect properties of the VIVE tracker's coordinate space rather than a misalignment of the OptiTrack and VIVE base frames. We observed that initial calibration attempts failed (resulting in scaling errors > 1 mm) in approximately 10% of cases, requiring a re-scan of the environment.

b) *Measuring Accuracy and precision:* We define the transformed VIVE positions $\hat{x}_i = R_{OV}x_i^V + t_{OV}$ and the per-sample absolute position error (APE)

$$e_i = \|x_i^O - \hat{x}_i\|_2. \quad (5)$$

Accuracy is summarized by MAE $= \frac{1}{N} \sum_i e_i$ and RMSE $= \sqrt{\frac{1}{N} \sum_i e_i^2}$, while precision (repeatability within a condition) is the standard deviation of $\{e_i\}$.

c) *Measuring Orientation Accuracy:* Unlike the positional data, where a static rigid-body transformation (rotation and translation) between the two coordinate frames was found using the SVD method, a similar direct alignment is not suitable for comparing orientation time series. While it is possible to compute a static rotational offset between the two frames based on an initial measurement pair, this approach is highly sensitive to noise. Any error in estimating this initial offset would propagate as a systematic bias throughout the entire dataset, making it difficult to distinguish between a static calibration error and the tracker's true dynamic tracking performance.

To complement the positional accuracy analysis, we evaluated the tracker's orientation tracking performance. A direct comparison of raw quaternions is sensitive to static rotational offsets between the VIVE and OptiTrack coordinate systems. To provide a more robust assessment of dynamic tracking performance, we therefore analyzed the **relative rotation angle** over time.

First, for each system (VIVE and OptiTrack), a stable orientation measurement from the beginning of a trial was selected as a reference quaternion, $q_{ref}$. For each subsequent
quaternion measurement, $q_i$, we calculated the relative rotation that transforms the reference orientation to the current orientation. This relative rotation is represented by the quaternion $q_{rel,i}$, computed as:

$$q_{rel,i} = q_i \cdot q_{ref}^{-1} \qquad (6)$$

where $q_{ref}^{-1}$ is the conjugate of $q_{ref}$. The scalar angle of this relative rotation, $\theta_i$, was then extracted from the real part (w) of $q_{rel,i}$:

$$\theta_i = 2 \cdot \arccos(|w_{rel,i}|) \qquad (7)$$

This procedure yielded two time series of relative rotation angles: $\{\theta_i^V\}$ for the VIVE tracker and $\{\theta_i^O\}$ for the OptiTrack system. The orientation error at each sample $i$ was then defined as the difference:

$$e_i = \theta_i^V - \theta_i^O \qquad (8)$$

where $\theta_i^O$ represents the interpolated OptiTrack angle. The overall orientation accuracy for a given trial was then summarized by the Root Mean Square Error (RMSE) of this angle difference.

d) Sampling rate: To determine the effective data update rate of the VIVE Ultimate Tracker, we developed a C++ application [23] to poll the device for new data packets at a high frequency (5 kHz). This high polling rate ensures that our measurement is not limited by the receiving software but by the tracker's own output capabilities. We recorded the high-precision system timestamp upon the arrival of each data packet and analyzed the time intervals (Δt) between consecutive packets that contained unique positional and rotational data.

Our analysis revealed that the time intervals between unique data updates consistently clustered around a mean of 8.33 ms, corresponding to a frequency of approximately 120 Hz. The distribution of these intervals showed a sharp peak at this value, with minor jitter attributable to standard operating system and network latencies.

It is important to clarify that this 120 Hz represents the effective data output rate at which the tracker provides new pose information to the end-user application. Whether this rate corresponds directly to the raw sensor fusion frequency or is influenced by internal processes such as upsampling or predictive filtering cannot be definitively determined without proprietary information from the manufacturer. However, for the purpose of this study—evaluating the tracker's performance as an integrated system—this 120 Hz is the relevant operational rate for data capture and was used in all subsequent experiments.

e) Latency: To estimate the latency of the VIVE Ultimate Tracker, we analyzed synchronized position data from both systems. The fundamental limitation of this analysis is the tracker's 120 Hz sampling rate. This creates a temporal quantization effect, meaning that new data is only available at discrete 8.3 ms intervals. The tracker's internal processing for a real-world event completes at a continuous point in time, but the resulting pose data is then reported in the next available discrete sample packet. This introduces a measurement uncertainty of up to 8.3 ms for any single event, as the true event time can fall anywhere within the preceding sampling

interval. Therefore, the physical latency is a continuous value, but our ability to measure it is quantized by this process.

Our methodology involved identifying high-acceleration events within the robotic movement data, specifically the start and stop phases of the cube patterns, which create sharp peaks in the velocity profile. For each of these events, we algorithmically located the timestamp of the maximum velocity peak in both the VIVE and OptiTrack datasets using the findPeaks function from the SciPy signal processing library. We then calculated the time difference between these corresponding peaks.

Across multiple trials and movement patterns, this measured time difference was consistently observed to be within a single sampling interval of the VIVE tracker (i.e., less than 8.3 ms). This indicates that the true latency of the positional output is on the same order of magnitude as its sampling period.

To form a complete estimate, we considered the baseline latencies of our measurement setup: the OptiTrack system has a known latency of 4.2 ms, and the network ping between our two computers was approximately 5 ms. This establishes a baseline of 9.2 ms for our ground-truth data path. Given that our measured time difference between the two systems was consistently smaller than 8.3 ms, we can infer that the VIVE tracker's true latency is closely aligned with this baseline. Therefore, we conservatively estimate the tracker's latency to be 10 ms ± 4.15 ms. The uncertainty bound of ±4.15 ms directly reflects the fundamental temporal resolution limit imposed by the 120 Hz sampling rate (half of the 8.3 ms interval).

f) Statistics: We employed a one-way Analysis of Variance (ANOVA) to compare differences between experimental conditions (e.g., lighting, velocity). To control for the family-wise error rate across multiple comparisons, we applied a Bonferroni correction. While the normality assumption was not strictly met, the large sample size ($n > 2000$) ensures the robustness of the ANOVA results due to the Central Limit Theorem [24]. In the figures, a * denotes a significant difference at $p < 0.05$, and ** denotes a significant difference at $p < 0.005$. No outliers were excluded from the analysis in this study.

### III. RESULTS

This section presents the results of our experimental evaluation. Our analysis revealed a notable dichotomy between the tracker's positional and orientational performance. Positional accuracy was found to be highly sensitive to a range of experimental parameters, and is therefore analyzed in detail across the first four subsections (III-A–III-D). In contrast, orientation accuracy was highly robust across all conditions; these findings are presented concisely in the final subsection III-E.

#### A. Impact of Lighting Conditions and Environmental Perturbations

Using the setup described in II-A, we evaluated how lighting variations and environmental perturbations affected the VIVE Tracker's accuracy and precision. For this experiment we used
Pattern B (Fig. 4) at an average velocity of 0.1 m/s. For each trial, data was captured over a duration of 120 seconds.

To characterize our lighting conditions, we measured both illuminance (in Lux) and color temperature (in Kelvin) at two locations in the experiment space using a Sinometer Lux Meter LX1010BS®. One position was at the surface of the tracker itself and another was on the opposing surface: the scene the tracker was facing.

* **Maximum brightness (ceiling lights + LED):**

- Tracker initial position: 210 Lux, 4100 K

- Opposing surface: 230 Lux, 4150 K

* **Ceiling lights only (no LED):**

- Tracker initial position: 65 Lux, 4100 K

- Opposing surface: 70 Lux, 4000 K

* **LED only (dimmed) for low-light conditions:**

- Tracker initial position: 30 Lux, 5850 K

- Opposing surface: 33 Lux, 5500 K

Initially, the tracker was tested under consistent bright lighting with the ceiling lights (at 3m height) on at full brightness and without the LED light source. We then tested under low lighting by turning off the ceiling lights and dimming the LED light source (placed 3m away of the initial tracker position) to create a dim environment. Dynamic lighting changes were introduced by keeping the ceiling lights on while varying the LED brightness, alternating between bright (ceiling lights on LED light off) and adding the LED up to maximal brightness. In a last case we started with a dim environment, increasing the brightness to create a bright environment.

Additionally, we conducted trials where the environment was perturbed by moving objects and having people pass through the tracker's field of view.

The results, shown in Fig. 6 and table I, demonstrate that low-light conditions significantly reduced both the tracker's accuracy and precision. In contrast, continuous changes in lighting from a bright baseline had minimal impact. However, during transitions from dark to bright, both the tracker's accuracy and precision were noticeably affected, likely due to partial darkness in the environment. Regarding environmental perturbations, the system exhibited more outliers and a slight increase in mean absolute error, though it remained relatively robust overall. Outdoor testing was not feasible due to the unreliability of natural light and the lack of nearby reference points, which rendered the tracker ineffective in such conditions.

<table><caption>TABLE I
TABLE SUMMARIZING THE ABSOLUTE ERROR [MM] AT DIFFERENT PERTURBATION SCENARIOS.</caption><thead><tr><th></th><th>Mean</th><th>Std. Deviation</th><th>Maximum</th></tr></thead><tbody><tr><td>Unperturbed, bright</td><td>2.392</td><td>0.957</td><td>6.340</td></tr><tr><td>Perturbed</td><td>3.750</td><td>1.996</td><td>12.584</td></tr><tr><td>Constant low light</td><td>10.635</td><td>3.213</td><td>19.210</td></tr><tr><td>Bright to brighter</td><td>3.734</td><td>1.615</td><td>12.119</td></tr><tr><td>Dark to bright</td><td>6.422</td><td>2.386</td><td>13.519</td></tr></tbody></table>

## B. Impact of Movement Velocity

We analyzed the effect of velocity on the VIVE Ultimate Tracker's performance using Pattern B (10 cm displacement),

<figure><img src="image_13.png" /><figcaption>Fig. 6. Boxplot showing the absolute error [mm] across different conditions during a 10 cm range of motion. Green represents optimal bright (Ceiling lights only (no LED), unperturbed conditions, orange indicates environmental perturbations, and yellow displays varying lighting conditions. Statistically significant differences from the optimal case are indicated by asterisks. (** p<0.005)</figcaption></figure>

which, as explained in Section II-A, allowed for a wide range of velocities. For each trial, data was captured over a duration of 90 seconds. To account for different movement dynamics, we examined both cube and circle patterns: cube patterns involve acceleration and deceleration, while circle patterns maintain a constant velocity.

Fig. 7 and Table II show the absolute tracking errors and velocity for both patterns across all tested velocities. The error increases significantly at velocities above 0.3 m/s for both movement types, with more outliers observed at higher velocities. As shown in Fig. 8, tracking accuracy declines as velocity increases, with a noticeable correlation between the error and velocity becoming evident, particularly in the accelerated cube movements around the 0.3 m/s threshold. Although the mean absolute error seems to decrease slightly between 0.1 and 0.2 m/s, the number of outliers increases, particularly in the cube movement. Since this movement accelerates beyond 0.3 m/s, reaching up to 0.4 m/s, the rise in outliers further supports this threshold. Although velocities below 0.3 m/s show statistically significant differences, these differences are unlikely to be practically relevant.

<table><caption>TABLE II
TABLE SUMMARIZING THE ABSOLUTE ERROR [MM] AT DIFFERENT MOVEMENT VELOCITIES AND DIVIDED BY ACCELERATED MOVEMENTS (CUBE) AND CONSTANT MOVEMENTS (CIRCLE).</caption><thead><tr><th></th><th>Velocity [m/s]</th><th>Mean</th><th>Std. Deviation</th><th>Maximum</th></tr></thead><tbody><tr><td rowspan="3">Cube</td><td>0.05</td><td>1.491</td><td>0.504</td><td>3.65</td></tr><tr><td>0.1</td><td>2.587</td><td>0.812</td><td>7.17</td></tr><tr><td>0.2</td><td>1.980</td><td>1.214</td><td>17.49</td></tr><tr><td rowspan="5">Circle</td><td>0.3</td><td>3.158</td><td>2.310</td><td>23.84</td></tr><tr><td>0.4</td><td>3.073</td><td>1.657</td><td>32.71</td></tr><tr><td>0.05</td><td>2.477</td><td>0.903</td><td>4.67</td></tr><tr><td>0.1</td><td>3.229</td><td>1.423</td><td>7.83</td></tr><tr><td>0.2</td><td>2.346</td><td>1.147</td><td>6.30</td></tr><tr><td>0.3</td><td>6.687</td><td>3.417</td><td>13.45</td></tr><tr><td>0.4</td><td>7.013</td><td>3.868</td><td>14.88</td></tr></tbody></table>
<figure><img src="image_14.png" /><figcaption>Fig. 7. Plot of unfiltered absolute tracking error (blue) and velocity (red) for cube (top row) and circle (bottom row) movement patterns at velocities of 0.1 m/s to 0.4 m/s.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 8. Box plot of the absolute error for cube and circle movements at velocities ranging from 0.05 m/s to 0.4 m/s. All comparisons between velocity groups and within movement types were found to be statistically significant (p<0.005). No additional asterisks are used for significance indication.</figcaption></figure>

### C. Impact of the range of motion

To better quantify the relationship between tracking volume and absolute error, we defined the 'distance to the calibration center' parameter. This parameter, illustrated in Fig. 9, measures the instantaneous distance between the tracker's current position and the origin of the calibrated workspace (the center of the 5 cm cube).

Let $x_0$ denote the calibration center (Fig. 9). We define the distance-to-center

$$d_i = \| \hat{x}_i - x_0 \|_2, \quad (9)$$

It is crucial to distinguish this metric from the overall Range

of Motion (ROM) or the total distance traveled. While ROM describes the general size of a movement, the 'distance to the calibration center' allows us to test whether the error is a cumulative, time-dependent drift or a static, spatial distortion. As seen in Panel C of Fig. 9, the error systematically increases as the tracker moves away from the center and decreases as it returns. This cyclical behavior demonstrates that the error is not a permanent drift but rather a function of the tracker's position within the pre-mapped environment, suggesting a form of spatial scaling or mapping error.

We evaluated the impact of ROM on tracking accuracy across patterns A–D (Fig 4), maintaining an average velocity of 0.1 m/s. For each trial, data was captured over a duration of 90 seconds. The results, shown in Fig. 10 Panel A, demonstrate a clear trend: larger displacements result in greater absolute error, with the highest errors observed for Fig. 10 pattern D (80x40x20 cm cube and 40 cm circle). In Fig. 10 Panel B, the scatter plot highlights the positive correlation between absolute error and distance to the calibration center, with a slope of 15.69 mm/m ($R^2$ = 0.73). The color gradient, representing velocity, confirms that this error is largely independent of velocity within the tested range. All numerical values are given in Table III.

<caption>TABLE III</caption>

<table><caption>TABLE SUMMARIZING THE VALUES OF DIFFERENT MOVEMENT SIZES. THE ABSOLUTE ERROR IS IN MM.</caption><thead><tr><td>Movement Size [cm]</td><td>Mean</td><td>Std. Deviation</td><td>Maximum</td></tr></thead><tbody><tr><td>5</td><td>2.556</td><td>1.194</td><td>12.299</td></tr><tr><td>10</td><td>2.922</td><td>1.094</td><td>8.163</td></tr><tr><td>20</td><td>3.975</td><td>1.512</td><td>11.953</td></tr><tr><td>40</td><td>6.757</td><td>2.271</td><td>12.114</td></tr></tbody></table>

To assess the effect of distance from the calibration center further, the robot performed end-effector movement pattern C
<figure><img src="image_16.png" /><figcaption>Fig. 9. (A) Calibration setup with the 5 cm cube defining the calibration center (red dot). (B) Visualization of the distances to the calibration center. (C) Correlation between distance to the calibration center and unfiltered absolute tracking error.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 10. (A) Box plot of absolute error [mm] for different displacement patterns: A (5 cm), B (10 cm), C (20 cm), and D (80x40x20 cm). (B) Scatter plot of absolute error vs. distance to calibration center [m], color-coded by velocity [m/s]. The dashed line represents a linear fit with a slope of 15.69mm/m and R² = 0.73. Asterisks were used to compare each increasing displacement size. ( ** p<0.005)</figcaption></figure>

(Fig. 4) while its movable base was displaced in a grid pattern away from the calibration center. Figures 11 and 12 display the results, showing a substantial increase in absolute error as the distance to the calibration center increases, but this error increase is not symmetrical. The most significant deviations occur along the negative x and z directions, as evidenced in both Fig. 11 and Fig. 12. The observed anisotropy suggests that the VIVE tracker tends to underestimate movement differently

in specific directions. Importantly, Fig. 11 highlights that while the overall accuracy remains fairly stable, the primary issue is scaling (with an underestimation of the movement captured), as the global measurements from OptiTrack and VIVE appear shifted rather than widely imprecise.

<figure><img src="image_18.png" /><figcaption>Fig. 11. 3D plot of measured displacements along the x and z axes, comparing tracking from the OptiTrack system (orange) and VIVE tracker (blue). The red point indicates the calibration center.</figcaption></figure>

To further investigate the effect of the range of motion, we evaluated the relative tracking error by using a second VIVE tracker attached to the robot's movable base (Fig. 1, (g)). This setup allowed us to measure the robot's movement relative to the base tracker. The results, shown in Fig. 12, indicate that while relative the tracking error was reduced in some cases, significant asymmetry and scaling issues remained.

In conclusion, displacement size has a clear and measurable impact on the VIVE Tracker's accuracy. Larger displacements, coupled with greater distances from the calibration center, consistently result in higher errors. However, this scaling behavior is not symmetrical; errors tend to be more pronounced in certain directions. This could be due to unsymmetrical distortions in the self-created map. While we also tested relative measurements, they did not significantly improve tracking
<figure><img src="image_19.png" /><figcaption>Fig. 12. Box plots illustrating the absolute tracking error for the VIVE Ultimate Tracker at different displacements from the calibration center in both global and relative movement modes. The displacements are shown along the x- and z-axes in relation to the calibration center. The significance comparisons were only made between relative and global (ns p>0.05, * p<0.05, ** p<0.005)</figcaption></figure>

accuracy, as the asymmetry and error trends persisted.

## D. Evaluation of performance in capturing human movement

a) Pick-and-place task: Fig. 13 illustrates the distribution of absolute errors across three trials for each tracker (knee, hip, shoulder, elbow, and wrist) during both static and walking pick-and-place tasks. In the static experiment, the knee and hip trackers show very narrow error distributions, with mean absolute errors of 1.62 mm ± 0.80 mm and 1.05 mm ± 0.49 mm, respectively. This low error can be attributed to the minimal movement at these joints during the static task, particularly as the calibration was done at the hip level where the subject stood.

The wrist tracker exhibits the highest absolute error, with a mean of 8.93 mm ± 5.01 mm, followed by the elbow (5.52 mm ± 3.64 mm) and shoulder (3.60 mm ± 2.62 mm). These results suggest that the greater the range of motion of the measured point on the body during the movement, the greater the error.

In the walking experiment, the error distribution shifts, with mean absolute errors increasing across all trackers. The hip and knee errors rise to 6.65 mm ± 5.07 mm and 7.49 mm ± 6.00 mm, respectively. The upper body trackers show larger errors, with the wrist reaching 13.68 mm ± 6.37 mm, the elbow 12.19 mm ± 6.46 mm, and the shoulder 9.98 mm ± 5.70 mm. When computing the relative error based on the hip tracker, all trackers show slightly improved accuracy but do not significantly improve in precision. This increase in error during the walking task underscores the effect of a larger range of motion on tracking accuracy. In this experiment, however, unlike in the experiment described in Section III-C, relative measurements slightly increased the accuracy for all trackers.

<figure><img src="image_20.png" /><figcaption>Fig. 13. Density plots of absolute errors for each body part (knee, hip, shoulder, elbow, and wrist) during pick-and-place tasks over 3 trials. The top row depicts the static condition, while the bottom row represents the walking condition. In the bottom row, errors of the to the hip tracker relative measured positions are shown in red.</figcaption></figure>

b) Fencing: The results of the high-speed fencing task, summarized in Table III-D.0.b and visualized in Fig. 14, indicate that the tracking error increases significantly with the dynamic nature of the motion. The mean absolute errors were highest for the wrist (17.52 mm) and elbow (12.31 mm), reflecting the larger range of motion and higher velocity at these points during the lunge. The hip and knee, which have less motion relative to the body's center, exhibited lower errors, at 9.21 mm and 6.62 mm respectively. The shoulder error was intermediate at 12.20 mm. The standard deviations are also indicative of the variability in tracking precision, with the wrist showing the greatest variability (10.73 mm), followed by the elbow (8.73 mm).
<caption>TABLE IV</caption>

MEASURED TRACKING ERROR [MM] AND RANGE OF MOTION (ROM) [M] AND MAXIMUM VELOCITY [M/S] FOR EACH BODY PART DURING THE FENCING EXPERIMENT.

<table><thead><tr><th></th><th>Mean</th><th>SD</th><th>Max. Vel [m/s]</th><th>RoM [m]</th></tr></thead><tbody><tr><td>Knee</td><td>6.624</td><td>5.557</td><td>2.78</td><td>≈ 1.1</td></tr><tr><td>Hip</td><td>9.208</td><td>6.766</td><td>2.25</td><td>≈ 1.0</td></tr><tr><td>Shoulder</td><td>12.202</td><td>6.869</td><td>4.72</td><td>≈ 1.4</td></tr><tr><td>Elbow</td><td>12.309</td><td>8.728</td><td>6.3</td><td>≈ 1.5</td></tr><tr><td>Wrist</td><td>17.516</td><td>10.725</td><td>9.34</td><td>≈ 1.6</td></tr></tbody></table>

<figure><img src="image_21.png" /><figcaption>Fig. 14. Density plots over three trials (each trial consisting of eight lunges) showcasing the distribution of tracking errors across different body parts during the fencing task.</figcaption></figure>

## E. Orientation Tracking Accuracy

To provide a complete 6-DoF performance analysis, we evaluated the orientation accuracy of the VIVE Ultimate Tracker. A key finding of this analysis is the stark contrast between positional and orientational performance. Unlike the positional accuracy, which varied significantly with experimental conditions, the orientation error was remarkably stable, with a measured mean RMSE of 0.36° ± 0.31° across all trials and conditions. Given that the nominal orientation accuracy of our multi-camera OptiTrack reference is of the same order of magnitude, this RMSE should be interpreted as a conservative upper bound on the VIVE Ultimate Tracker's true orientation error, rather than a precise validation of the 0.36° specification.

To illustrate this robustness, we present the data from the fencing experiment. This task was chosen specifically because it induced the largest positional errors observed in our study (Section 3.4), thereby representing the most challenging scenario for the tracker's sensor fusion capabilities.

The orientation performance during this task is summarized in Table V. Despite substantial angular rotations, with a range of motion exceeding 110 degrees for the knee, the orientation RMSE remained below 0.5 degrees for all body segments. Furthermore, the Pearson correlation coefficient with the Opti-Track ground truth was >0.998 for all trackers.

<table><caption>TABLE V ORIENTATION TRACKING PERFORMANCE DURING THE FENCING TASK</caption><thead><tr><th></th><th>RMSE [deg]</th><th>Std. Dev. [deg]</th><th>Rotation RoM [deg]</th></tr></thead><tbody><tr><td>Knee</td><td>0.44</td><td>0.43</td><td>40.8</td></tr><tr><td>Hip</td><td>0.23</td><td>0.23</td><td>52.6</td></tr><tr><td>Shoulder</td><td>0.35</td><td>0.34</td><td>97.9</td></tr><tr><td>Elbow</td><td>0.21</td><td>0.21</td><td>89.2</td></tr><tr><td>Wrist</td><td>0.30</td><td>0.21</td><td>111.9</td></tr></tbody></table>

Given the consistent high performance observed in this demanding condition, as well as in all other tests (where RMSE remained similarly low, < 0.4°), a detailed condition-by-condition breakdown of orientation error is omitted to avoid redundancy.

## IV. DISCUSSION

### A. Novelty and Significance of the Contribution

The HTC VIVE Ultimate Tracker presents a cost-effective, user-friendly solution for certain specific motion tracking scenarios. Utilizing inside-out tracking, it simplifies the setup process by removing the need for external cameras, and simple open-source software [21], [23] can be used to collect data.

This study presents a comprehensive quantitative validation of the HTC VIVE Ultimate Tracker. While focused on this specific commercial device, the evaluation provides critical insights into the performance envelope of the emerging class of stand-alone, inside-out trackers that perform on-board sensor fusion. By establishing a rigorous, robot-driven assessment protocol, we aim to move beyond simple 'black box' testing to characterize both the device-specific behaviors and the broader limitations inherent to consumer-grade VIO/SLAM technology.

While the performance evaluation of a commercial device can be a standard approach, the novelty of this work extends beyond a simple benchmark in two additional key areas.

First, we establish a rigorous and transparent methodological blueprint for assessing such devices. By using a high-precision industrial robot to generate repeatable ground-truth motions and systematically varying key environmental and kinematic parameters (lighting, environmental perturbations, velocity, and range of motion), our methodology provides a template for future validation studies of other stand-alone trackers.

Finally, and most significantly, our contribution is the identification of the tracker's specific operational strengths and failure modes. The key insights are: (1) the clear distinction between the highly robust, IMU-driven orientation tracking and the more fragile, SLAM-driven position tracking; and (2) the characterization of the dominant positional error source as a non-symmetrical scaling issue that correlates with the distance from the calibration origin. This latter point is a critical and non-obvious finding that provides practical, actionable guidance for any researcher aiming to use this technology, transforming it from a "black box" into a characterized scientific instrument. Additionally, our results indicate that, within the accuracy limits of the reference system, orientation errors remain small and stable, suggesting that orientation tracking is unlikely to be the limiting factor for typical wearable-tracker applications.

### B. Generalization to Other Inside-Out Trackers and Limitations

To interpret our results correctly, it is necessary to distinguish between findings that likely generalize to the broader category of inside-out trackers and those specific to the VIVE Ultimate Tracker. Regarding generalizable characteristics, the significant degradation of accuracy in low-light and dynamic environments is a fundamental characteristic of camera-based SLAM systems. This dependency on visual features is consistent with independent evaluations of comparable consumer-grade devices, such as the Oculus Quest 2 [25] and Rift S [26], which exhibit similar sub-centimeter accuracy profiles but
comparable sensitivity to environmental conditions. Despite these limitations, such inside-out systems have been successfully validated for human motion analysis in rehabilitation contexts [27], supporting the feasibility of this technological category for biomechanics. Similarly, the observed robustness of orientation tracking compared to the fragility of positional tracking is a common trait in Visual-Inertial Odometry (VIO) systems. Orientation is heavily stabilized by high-frequency IMU data, while position tracking is fundamentally dependent on the continuous detection and matching of visual features in the environment. Consequently, position estimation is inherently more sensitive to external factors such as lighting conditions and texture availability, whereas orientation remains robust due to its reliance on internal inertial sensors.

Other findings occupy a middle ground between general and device-specific characteristics. The scaling error we observed, where error increases with distance from the calibration center, is related to scale drift and map deformation—phenomena well-documented in the VIO/SLAM literature [18], [28]. While theoretically constrained by the stereo camera baseline, SLAM systems often exhibit residual scale ambiguity due to minor camera calibration imperfections or IMU bias integration. However, the pronounced anisotropy we observed—where movements are underestimated significantly more in specific axes (Fig. 11)—suggests a specific systematic bias in the VIVE Ultimate Tracker's factory calibration or sensor fusion tuning, rather than random drift. Therefore, while users of any inside-out tracker should be vigilant for scale factors, the specific asymmetrical distortion pattern reported here is likely particular to the current hardware and firmware iteration.

* **Generalizable Traits (Inside-Out Category):** Consistent with comparable SLAM systems, we observed significant accuracy degradation in low-light and dynamic environments. Furthermore, the device exhibits the characteristic dichotomy of VIO systems: robust orientation tracking (stabilized by the IMU) versus fragile position tracking (dependent on visual features).

* **Device-Specific Findings (VIVE Ultimate Tracker):** Unlike general scale drift, the tracker displays anisotropic scaling errors, where movement is systematically underestimated on specific axes likely due to factory calibration biases.

Regarding the limitations of this study, we must acknowledge that our evaluation was restricted to a single hardware unit and a controlled laboratory environment. While the robot allowed for precise repeatability, outdoor testing was not feasible due to lighting constraints. Furthermore, as the device runs closed-source firmware, the exact cause of the scaling issues—whether due to camera calibration parameters or SLAM map optimization—cannot be definitively isolated without manufacturer transparency. Finally, it should be noted that the performance characteristics reported here reflect the firmware version available at the time of testing; future software updates and algorithmic improvements from the manufacturer could potentially address the specific scaling anomalies and tuning behaviors observed in this work.

### C. Impact of Operational Factors

Although the VIVE Tracker exhibited good overall accuracy for general applications—such as tracking human motion during Activities of Daily Living (ADL)—it is not suitable for tasks that require sub-millimeter accuracy. Additionally, our evaluation revealed that the tracking performance is strongly affected by several factors.

Range of motion emerged as the dominant factor affecting the tracker's accuracy. Larger displacements consistently resulted in higher absolute errors, especially as the distance from the calibration center increased. This loss of accuracy can be explained by a non-symmetrical scaling issue, with movement being underestimated differently in different directions. Therefore, users should verify scaling before data collection and, if necessary, apply scaling factors, and re-calibrate the system if scaling issues are observed already in small displacements.

Lighting conditions and environmental perturbations also impacted performance. It is important to frame these specific experiments as illustrative; they are designed to reveal the potential sensitivities of the camera-based SLAM system rather than to provide universally generalizable error metrics. In our tests, lighting emerged as the most critical factor, with absolute errors increasing substantially in low-light environments. The system also remained robust when objects were moved or people passed through the scene, though this did increase the number of outliers. These findings highlight key operational vulnerabilities, but we explicitly acknowledge that the precise magnitude of these effects is not generalizable and will vary depending on the specific characteristics of any given environment. Outdoor usage was not feasible, likely due to inconsistent lighting and a lack of nearby visual features for tracking. Velocity had a relatively minor impact on tracking accuracy. While errors noticeably increased when velocities exceeded 0.3 m/s, they remained within a reasonable range throughout the tested velocities of up to 0.8 m/s.

A key aspect of our findings is the significant standard deviation observed in several conditions, which is often comparable to the mean error itself. Rather than indicating a flaw in the measurement methodology, we interpret this variability as a fundamental characteristic of the consumer-grade, camera-based SLAM tracking technology evaluated. Unlike high-end, deterministic systems (e.g., optical trackers), the VIVE Tracker's performance is contingent on the real-time success of its visual feature matching algorithm. Factors such as minor changes in lighting, motion blur at high velocities, or temporarily feature-poor environments can lead to momentary tracking failures, which manifest as the transient error spikes or outliers observed in our data.

Therefore, the mean absolute error should be interpreted as the tracker's expected accuracy under typical operation, while the standard deviation serves as a crucial metric for its robustness and consistency. This large variance is a significant finding for potential users, as it highlights that while the tracker may achieve a certain average accuracy, it is also prone to occasional, larger deviations that must be accounted for in applications sensitive to outliers or requiring consistent precision.
The lack of detailed technical specifications and hardware information from the manufacturer, such as latency and sampling rates, presents challenges for academic research, where precise performance data are essential.

Despite these limitations, the VIVE Tracker remains a versatile and accessible tool for a wide range of motion tracking applications. However, its sensitivity to numerous parameters requires careful consideration and may limit its utility in environments where these factors cannot be adequately controlled.

## V. CONCLUSION

The HTC VIVE Ultimate Tracker proves to be an interesting tool for motion capture, particularly in applications where ease of use and flexibility are essential. While it does not achieve the sub-millimeter accuracy and precision of high-end optical motion capture systems with external cameras and reflective markers such as OptiTrack, it provides sufficient accuracy for a wide range of motion tracking scenarios. Yet, the tracker's tracking accuracy is influenced by factors such as lighting conditions, environmental changes, and velocity, but mostly by displacement size, with scaling issues presenting the most significant challenge. Despite these limitations, the VIVE Tracker's affordability and ease of setup make it a valuable tool for researchers and developers working in fields where high accuracy is not the primary requirement. Users must, however, remain vigilant about potential scaling errors and ensure proper calibration to maximize the tracker's accuracy. As the software and hardware continue to evolve, the VIVE Ultimate Tracker may become an increasingly reliable tool for motion tracking applications outside the realm of virtual reality. Ultimately, this work provides a rigorous and transparent methodological blueprint that can be adapted for the critical evaluation of future vision-based stand-alone tracking technologies.

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