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# RGB-D versus omnidirectional visual SLAM in humanoid robot positioning

Antoine Caillot, Antoine André, Thomas Duvinage, Guillaume Caron

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## ► To cite this version:

Antoine Caillot, Antoine André, Thomas Duvinage, Guillaume Caron. RGB-D versus omnidirectional visual SLAM in humanoid robot positioning. IEEE-RAS 24th International Conference on Humanoid Robots (HUMANOIDS 2025), Sep 2025, Séoul, South Korea. hal-05193891

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https://hal.science/hal-05193891v1

Submitted on 30 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
RGB-D versus omnidirectional visual SLAM
in humanoid robot positioning

Antoine Caillot<fnref n="1" />,*, Antoine N. André<fnref n="1" />, Thomas Duvinage<fnref n="1" />, Guillaume Caron<fnref n="1" />,<fnref n="2" />

**Abstract**— Visual Simultaneous Localization and Mapping (V-SLAM) with an color plus Depth (RGB-D) camera is the gold standard to help correcting humanoid robots' drift while walking toward a goal pose indoors. But, the bounded range and Field of View (FoV) of these cameras constrain the use cases for humanoids to few changes in the environment. At the same time, compact omnidirectional cameras capable of a 360° FoV are spreading quickly at low cost, offering more possibilities to the V-SLAM system to observe reliable features whichever the robot orientation.

Thus, this paper investigates the V-SLAM with a 360° camera versus an RGB-D one in the humanoid head for correcting the walking drift of a baseline walking controller over several meters. The *Baseline Biped Positioning* hence made is evaluated regarding final pose accuracy improvement and robustness to environmental changes by leveraging multiple sets of real experiments on a 1.8 m tall humanoid robot.

# I. INTRODUCTION

## A. Context and motivation

As any mobile robot, humanoids drift while walking toward a goal location based solely on proprioceptive sensors [1]. Such drift can prevent the humanoid arms to reach their own target poses for grasping a door handle, an object or else [2]. Hence, using exteroceptive sensors to correct the robot drift based on its perception of the environment has been receiving a lot of attention for several decades [3].

Putting a camera in the robot's head allows to reach a goal defined by an image with visual servoing [4] or visual path following [5] when the goal is too far from the initial humanoid pose. However, the most spread approach is leveraging Visual Simultaneous Localization and Mapping (V-SLAM) either without [6] or with [7] a prebuilt map of the environment, the latter generally ensuring a lower Absolute Pose Error (APE) than the former, if the environment remains similar enough for the V-SLAM system between the first mapping and the use of the prebuilt map.

There is a large variety of camera types [8] of which many of them can be used for Simultaneous Localization and Mapping (SLAM) [9] but, indoor, the most common in the literature is the RGB-D camera [10] (Fig. 1b) due to its ability to capture a depth map in addition to the color (RGB) image that fixes the local scale factor of the map. On the other hand, compact cameras implementing a dual-fisheye configuration (Fig. 1a) allowing a 360° FoV have become wide spread in the computer vision community, even integrated in the open-source V-SLAM system now named

<figure><img src="image_4.png" /><figcaption>Fig. 1: Humanoid robot equipped with a 360° camera and RGB-D camera mounted on its head.</figcaption></figure>

StellaVSLAM [11], an extension of ORB-SLAM2 [9] to the use of equirectangular images representing the full spherical FoV in the image plane. Yet, their use for robot positioning is very new [12], even absent of humanoid robots to our knowledge.

Since the 360° FoV offers more robustness to localize the camera when scene changes occur than the conventional FoV of RGB-D cameras [13], this article integrates a compact 360° omnidirectional camera to a human adult-size humanoid robot (Fig. 1a) for its self-localization with V-SLAM to correct the walking drift and evaluates the contribution over the use of a RGB-D camera.

## B. Related works

1) Visual SLAM: Beyond enabling the localization of objects in the environment [14], cameras can also facilitate the localization of the robot itself.

RGB-D cameras has become essential for indoor robotics due to their ability to provide both visual (RGB) and depth (D) information, improving SLAM accuracy since the advent of the first Microsoft Kinect. Indeed, directly capturing depth data enabled real-time dense 3D reconstructions, that are crucial for tasks like navigation and mapping in unstructured

<footnote n="1">¹CNRS-AIST Joint Robotics Laboratory (JRL), IRL, Tsukuba, Japan.</footnote>

<footnote n="2">²MIS laboratory, University of Picardie Jules Verne, Amiens, France.</footnote>

<footnote>*Corresponding author. Email: antoine.caillot@cnrs.fr</footnote>
environments [15], [16], [17].

The depth information from RGB-D cameras is particu-
larly valuable in low-texture areas, where visual features are
rare and poor. KinectFusion [18] was a pioneering system
that illustrated how depth data could facilitate accurate, dense
3D indoor models in real time.

More advanced systems, such as ORB-SLAM2 [9] and ElasticFusion [19], refined RGB-D SLAM, improving pose estimation and loop closure, balancing scale drift. However, RGB-D cameras are limited by their range, sunlight perturbations and no depth measured on glossy or infrared absorbing materials, motivating integration with depth prediction algorithms, which infer depth per RGB pixel.

More recently, Neural Radiance Field (NeRF)-based V-SLAM approaches have shown improved performance in scene reconstruction [20], [21], [22]. While these methods are promising, they typically demand significant computational resources, limiting their suitability for real-time applications on humanoid robots. Furthermore, none of the above mentioned state-of-the-art approaches support the use of 360° cameras.

RGB-D cameras are limited by their narrow FoV, mo-
tivating integration with cameras capable of 360° views.
The most compact ones are implemented as a dual-fisheye
camera [23]. Recent models are of two centimeters thickness
(see the Insta 360 One X2 in Fig. 1a), thus comparable
to a monocular camera. The proximity of the two fisheye
viewpoints even makes the single viewpoint approximation
acceptable enough to transform the two captured fisheye
images to a single equirectangular image and obtain accurate
motion estimation results [24].

Such 360° FoV cameras are particularly suitable in envi-
ronments where narrow FoV cameras face challenges with
sparse features [25], [11], [26]. By covering the entire sur-
rounding scene, spherical cameras offer robust tracking and
improved feature matching [13], essential for accurate SLAM
performance in large-scale and dynamic environments [27].

2) Visual SLAM for humanoids: The use of Visual Si-
multaneous Localization and Mapping for walking control
has already been explored in the literature. For instance,
Scona et al. [28] employed V-SLAM for the locomotion of
the humanoid robot NASA Valkyrie. The authors utilized
a stereoscopic camera to feed an RGB-D V-SLAM system
(an extension of ElasticFusion [19]), which in turn provided
input to fuse within the kinematic-inertial state estimator to
mitigate drift and enable the robot to consistently reach its
goals. Such fusion scheme was necessary to improve the
accuracy of the V-SLAM for pose-based navigation, at that
time limited due to vibrations when the humanoid feet hit
the floor, low camera framerate (gap between consecutive
views) and long exposures (motion blur).

In case drift remains, at least locally despite some loop
closure, a workaround is to pre-map the environment and
define the goal poses for the humanoid to reach within that
map [29]. But to keep an accurate positioning even if the goal
to reach is not at a keyframe, live mapping to complement a
pre-built map allows to be not only accurate near keyframes

<figure><img src="image_5.png" /><figcaption>Fig. 2: Block diagram of the proposed positioning system. User's input a **target position** that will be transformed by the *Baseline Biped Positioning* into a **relative target position** for the *Baseline Walking Controller*. The V-SLAM system provides the robot's current absolute pose, which is used to compute the scale factor and to correct the relative target position. The *Baseline Walking Controller* uses proprioceptive sensors to estimate its current state and to compute the commands sent to the robot's actuators.</figcaption></figure>

but also in-between them [7].

C. *Contributions*

The core idea of this work is to compare different vi-
sion modalities for humanoid robot positioning based on
V-SLAM, integrated in the proposed *Baseline Biped Po-
sitioning* system. Rather than comparing various V-SLAM
algorithms, we focus on exploring whether monocular RGB
360° cameras provide performance and robustness benefits
over more commonly used RGB-D cameras. This article
includes the following contributions:

* an all-in-one solution for absolute humanoid robot po-
  positioning within its environment,

* a repeatable benchmarking protocol to evaluate the
  performance of the *Baseline Biped Positioning* system,

* an experimental comparison of two vision modalities: a
  monocular RGB 360° camera and a RGB-D camera,

* a robustness analysis under environmental changes,
  demonstrating how each camera modality performs in
  varied conditions.

Through real-world experiments on a humanoid robot,
this article aims to provide practical insights and trade-
off analysis between vision modalities for V-SLAM-based
humanoid navigation.

II. METHODOLOGY

The implementation of the proposed positioning system for humanoid robot is composed of three main modules, as illustrated in Fig. 2. The first module is the *Baseline Walking Controller*, which is based on proprioceptive sensors and
enables the robot to walk. The second module is the V-SLAM system, which provides localization of the robot within its environment. The final module is the positioning system, which processes user commands and integrates feedback from the V-SLAM module to issue relative target commands to the walking controller, depending on the robot's position in its environment. Each module is detailed in the following subsections.

## A. Humanoid robot walking controller

In the proposed positioning system for humanoid robots, the displacements are handled by the *Baseline Walking Controller* [30], which enables an easy control to input desired position for the robot. This solution embeds multiple key components for the humanoid walk, such as the trajectory generation, the planning of the footsteps, and the underlying constant estimation of the robot state. The controller is based on a model predictive control (MPC) that takes into account the robot's dynamics and constraints. The MPC is designed to minimize the difference between the desired and actual trajectories of the robot's center of mass and feet positions. However, and as stated in the introduction of this article, the actual state estimation, based on the proprioceptive sensors, can only provide a rough estimate of the actual robot position, relative to its starting position. This relative pose estimation, coupled with its lack of accuracy and drift, motivates the need to provide a more precise and absolute localization of the robot in its environment.

## B. Visual SLAM

To that extent, we propose to use a common lightweight and efficient V-SLAM system designed for real-time applications, *StellaVSLAM* [11]. As described in the introduction of this work, this framework is based on a feature-based approach, extracting and tracking keypoints over time to estimate the camera pose. The system uses a combination of visual odometry and loop closure detection to improve the accuracy of the pose estimation. Furthermore, *StellaVSLAM* proposes to use as input various types of cameras, such as RGB-D ones or monocular 360° cameras.

To align the output of the V-SLAM system with the real-world environment in which the robot operates, it is necessary to construct a map of the environment. This map enables the robot to localize itself in an absolute and repeatable manner, and also fixes the scale factor between acquired map and the real environment—which is mandatory when using monocular vision systems. Although this mapping step is not strictly required when using depth-aware cameras (e.g., RGB-D sensors), it significantly improves the accuracy of camera pose estimation and facilitates scale estimation between the reconstructed map and the real environment. In the proposed pipeline of the *Baseline Biped Positioning*, we leverage a feature of *StellaVSLAM* that allows for map creation through exploratory motion in the environment. Note that the cameras used during the map acquisition is also used during deployment in the humanoid positioning pipeline.

The scale factor can be retrieved from the acquired map through various methods, and we propose to study two of them, that are both practical, accurate and easy to implement. The first relies on a motion capture (MoCap) system and spatial anchor points. Although this method can achieve high precision, it requires a MoCap system, which can be tedious to put in practice depending on the environment where the robot will operate, limiting its practicality. The second method is based on using solely the acquired map of the environment. This approach is easier to deploy and does not require a MoCap setup, though it may results in lower accuracy due to the sparsity of the map produced by the V-SLAM. Employing two complementary methods allows to cross-validate results. Additionally, this comparative approach offers valuable insight into the trade-offs of each method, which is beneficial for future applications.

1) **Common anchor points:** A first approach to estimate the scale factor involves comparing distances measured using the V-SLAM system with those obtained in the real world. By using a MoCap system, it would have been possible to align trajectories and estimate the scale factor, as proposed by the Evo tools [31]. While recording the whole trajectory done by the robot during the mapping phase would lead to a precise estimation of the scale factor, it requires to handle a large amount of data, and a good synchronization between the MoCap and the V-SLAM systems. On the other hand, we propose to use a simpler approach that doesn't compromise the accuracy of obtained scale factor.

This approach relies on the acquisition of anchor points that are identifiable both in the V-SLAM trajectory and the MoCap one. Anchor points are defined as locations in the environment where the camera remains stationary for a few seconds, allowing for an easy and accurate identification in both trajectories. We denote these N anchor points as $A_i^{SLAM}$ for the points in the V-SLAM trajectory and $A_i^{MoCap}$ for those in the MoCap one. From these anchor points, two sets of pairwise distances between all anchor points of size $M = \binom{N}{2}$ can be computed, where $\binom{N}{2}$ is the binomial coefficient. The scale factor s is then calculated as the average ratio between each accounted distance:

$$s = \frac{1}{M} \sum_{0 \le i < j \le N-1} \frac{\|A_i^{MoCap} - A_j^{MoCap}\|}{\|A_i^{SLAM} - A_j^{SLAM}\|} \quad (1)$$

Since the distances computed from the MoCap trajectory are expressed in meters, the scale factor s enables the conversion of distances in the V-SLAM trajectory to real-world metric units.

2) **Point cloud analysis:** Another approach to estimate the scale factor is to use two parallel planes separated by a known distance in the real world. In indoor navigation scenarios, three distinct parallel planes are typically observed: the floor, the ceiling, and the plane defined by the camera poses, as shown in Fig. 3. However, the floor is often occluded by the robot itself and generally contains few feature points, making its extraction difficult and prone to higher estimation errors. Therefore, the ceiling and the plane formed
<figure><img src="image_6.png" /><figcaption>Fig. 3: Generated point cloud using the V-SLAM system (in red) aligned with a dense point cloud of the environment. Three main planes can be identified: the floor, the ceiling, and the plane defined by the camera poses (green).</figcaption></figure>

by the camera poses are the most suitable candidates. The real-world distance between the ceiling and the camera can then be measured and divided by the corresponding distance between the two planes in the point cloud to determine the scale factor.

### C. Positioning system

As previously presented, the *Baseline Biped Positioning* module (see Fig. 2) serves as the interface between the V-SLAM system and the *Baseline Walking Controller*, providing the robot pose commands to achieve its target position and ensure the correction of the robot's drift. When a target position is provided to the system, the *Baseline Biped Positioning* is initialized by taking the current position estimated by the V-SLAM system as its origin, and stores the target position. As the robot navigates through the environment, its position is continuously estimated by the V-SLAM system, and the remaining trajectory to the target is dynamically updated at the end of the trajectory. Once the *Baseline Walking Controller*—which only operates under the constraints of the robot's proprioceptive sensors—completes its task, the *Baseline Biped Positioning* issues a new correction command based on the position error of the robot. In this work, this correction is limited to a single iteration at the end of each sub-trajectory provided to the humanoid robot, allowing for a better analysis and comparison between the two studied vision modalities. However, this correction process can be repeated multiple times to improve the robot's accuracy in reaching the target.

## III. EXPERIMENTS

In this section, we present the experimental setup and protocol used to validate the proposed *Baseline Biped Positioning* framework. We then analyze the performance of the *Baseline Biped Positioning* in terms of accuracy and robustness, comparing the results obtained with both cameras.

### A. Experimental setup

To validate the proposed *Baseline Biped Positioning* framework, multiple experiments are put in practice. The goal is to evaluate the gain of performance in terms of

accuracy and robustness of the humanoid robot's positioning system to reach a desired target pose when using two different vision modalities: a monocular omnidirectional camera (Insta 360 One X2¹) and a RGB-D camera (Microsoft Azure Kinect<fnref n="2" />). Each of these cameras are mounted on the humanoid robot (HRP robot), as presented in Fig. 1a and b. While the omnidirectional camera offers a full 360° FoV but without any depth sensor, the RGB-D camera provides a depth map but with a limited field of view (75 × 65°).

The robot is set to navigate in a 6.0 × 4.2 m<fnref n="2" /> indoor environment, with one part of the experimental room containing many visual features, and the other part with less features to track, as it can be partially seen in dense point cloud in Fig. 3. This allows to evaluate the performances of the *Baseline Biped Positioning* in different scenarios, thus highlighting the differences between the two tested cameras.

On the software side, the *Baseline Biped Positioning* framework is based on the *Baseline Walking Controller* [30] for the locomotion and the *StellaVSLAM* framework [11] for the V-SLAM part, which can handle both monocular omnidirectional and RGB-D cameras to estimate the camera pose in the environment. The correction of each robot's trajectory is based on the methodology described in Sec. II.

Prior to the execution of the experiments, a mapping of the environment is performed using both cameras, allowing the *Baseline Biped Positioning* to localize the robot in its environment when doing the proposed sets of experiments. To build the map, we followed the methodology described in [13]. The camera was mounted on the humanoid robot, which was slightly elevated above the ground using a lifter to facilitate handling. As a result, the camera maintained a constant altitude of approximately 2 m above the floor. We then executed a trajectory designed to avoid pure rotations without translation, favoring slow movements and repeatedly passing through the same areas to enable loop closures, while ensuring thorough coverage of the environment. In the case of the omnidirectional camera, we also included stop points where the camera remained stationary for a few seconds.

<footnote n="1">¹https://www.insta360.com/product/insta360-onex2</footnote>

<footnote n="2">²https://azure.microsoft.com/en-us/products/kinect-dk</footnote>
<figure><img src="image_7.png" /><figcaption>Fig. 4: Set of trajectories sent to the robot, with targeted poses to be corrected by the *Baseline Biped Positioning*. **4a** presents the 1, 3, and 4 set of targeted poses (with an initial robot pose at (0,0)) and **4b** presents the second set of targeted poses, mirror of set 1 (with an initial robot pose at (5,0)).</figcaption></figure>

These stationary points serve as shared anchors with the MoCap system to estimate the scale factor, as detailed in Sec. II-B.

## B. Experimental protocol

The protocol to evaluate the performances and differences between the two cameras for positioning task is designed as follows: four main sets of trajectories are sent to the humanoid robot, to ensure a good coverage of the environment where the mapping of the V-SLAM system has been created beforehand. The first set of experiments, described in Fig. 4a (exp. 1.i for $i \in [1, 4]$), consists of 4 target poses, where the robot navigates from the left to the right part of the environment. The second set of experiments (presented in Fig. 4b, and referred as exp. 2.i for $i \in [1, 4]$) is the mirror of the first set of experiments, and is aimed to navigate from the right to the left part of the environment. For these two first sets of experiments, the robot pose is corrected at the end of targeted trajectory using the proposed *Baseline Biped Positioning*. The third experiment (exp. 3) is a squared

<figure><img src="image_8.png" /><figcaption>Fig. 5: Robot pose and estimated pose by the V-SLAM at the end of a targeted trajectory to reach pose t. $d_1$ refers to the traveled distance by the robot, $d_2$ the distance applied by the *Baseline Biped Positioning* to correct the robot's pose.</figcaption></figure>

trajectory of 2.1 m where the robot pose is corrected only at the last corner (at pose (0,0)) and is repeated as experiment 4 (exp 4), where the robot pose is corrected at each corner of the square (as presented in Fig. 4a).

Finally, each of these experiments (10 trajectories), are repeated twice, once with the omnidirectional camera and once with the RGB-D camera. To better evaluate the performance of both tested cameras, and isolate the impact of the camera on the positioning task, the robot is set to perform only translational movements, without any rotation.

## C. Performance analysis

In this section, we present the results of the experiments conducted to evaluate the performance of the proposed *Baseline Biped Positioning* framework. We analyze the accuracy of the *Baseline Biped Positioning* system using both the omnidirectional and RGB-D cameras.

1) *Metrics used for the evaluation:* The gain of performance when using the proposed *Baseline Biped Positioning* applied to humanoid robot positioning both with the omnidirectional and the RGB-D cameras is quantified using the classical APE and Relative Pose Error (RPE) metrics. At the end of each trajectory, the robot reaches the target pose with an error proportional to the distance it has traveled as $d_1 \cdot \epsilon_r$, where $\epsilon_r$ represents the relative positioning error of the humanoid robot. Fig. 5 highlights this error with the robot positioning error circle, centered on its actual end-of-trajectory pose $r_1$. The V-SLAM system estimates the robot's pose with its own error of $\epsilon_s$ (different for each camera used), and calculates the correction to apply to reach the target pose $t$. From this, the robot should reach the pose $r_2^*$ presented in Fig. 5, but actually reaches the pose $r_2$, with an estimated pose by the V-SLAM at $s_2$.

Therefore, the following metrics are computed:

* $\epsilon_s = |rs|$, the APE of the V-SLAM

* $\epsilon_r = |rt|$, the APE of the robot
<figure><img src="image_9.png" /><figcaption>Fig. 6: Box plot for APE of V-SLAM using both RGB-D and omnidirectional cameras in the three main scenarios covered in the experiments.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 7: Box plot presenting the gain (%) in APE after using the *Baseline Biped Positioning* to correct the robot's pose. The three main scenarios are presented with their respective gain of performance for both cameras.</figcaption></figure>

Furthermore, each of these APE metrics can be broken down into two parts, i.e. the uncorrected and the corrected ones, allowing to compute the gain of performance when using the *Baseline Biped Positioning* system:

$$ \text{gain} = \frac{\varepsilon_{uncor} - \varepsilon_{cor}}{\varepsilon_{uncor}}. \qquad (2) $$

From all the trajectories of all experiments, an overall robot RPE 13.48% of its traveled distance was found. Before applying any correction coming from the *Baseline Biped Positioning*, the robot's mean APE was 0.36 m.

2) *Omnidirectional camera results:* When using the omnidirectional camera in the *Baseline Biped Positioning*, the robot's mean APE fell to 0.16 m after correction, highlighting the high potential of using an omnidirectional camera

for positioning tasks. When examining the major scenarios covered during the experiments, the V-SLAM APE remains consistent with a median value below 0.2 m, as presented in Fig. 6. This demonstrates the robustness of the *Baseline Biped Positioning* system when coupled with an omnidirectional camera, as 360° FoV of the camera allows to retrieve features from the whole environment. As highlighted in Fig. 7, this results in an improvement of the robot's APE, after applying a pose correction step, of 54.13% on average.

3) *RGB-D camera results:* On the other hand, when using the RGB-D camera, the robot's mean APE is 0.24 m, which is above the mean APE provided by the omnidirectional camera. This loss of performance is mainly due to the limited FoV of the RGB-D camera used. Indeed, and as highlighted in Fig. 6, the V-SLAM APE for the RGB-D camera is more variable depending on its position in the environment. As presented in Fig. 7, this results in a more uneven gain of performance (ranging from 40% to 84%), with an mean gain of 43%.

4) *Wrap-up:* Reading from Table I, which presents all APE values, both uncorrected and corrected by *Baseline Biped Positioning*, along with the corresponding improvement in trajectory accuracy, it is evident that placing the RGB-D camera in a favorable part of the environment with many trackable features results in a significant accuracy gain. However, this reliance on rich visual features makes the positioning system less robust in environments with fewer observable cues.

This highlights the importance of the camera's FoV in the humanoid robot positioning task, as the RGB-D camera is limited to a 75° FoV, which can lead to a loss of features in the environment and therefore a less accurate localization. While the omnidirectional camera is unable to provide any depth information, its 360° FoV allows to retrieve features from the whole environment, leading to a more accurate localization. This is particularly important in positioning tasks, where the robot needs to be able to localize itself accurately in its environment.

## D. Robustness analysis

In this section, we present a robustness analysis of the *Baseline Biped Positioning* framework to evaluate the impact of environmental changes on the positioning task by modifying the environment after the mapping step.

1) *Experimental protocol:* To better evaluate the contribution of each camera in the positioning task, a robustness analysis to changes in the environment is performed. This analysis is done by sending desired positions 1.2, 1.4, 2.2 and 2.4 to the robot (as described in Sec. III-B). While using the same mapping as for the previous experiments (described in Sec. III-B), multiple changes in the environment are made (such as moving furniture). The goal here is to better mitigate the impact of the environment on the robot's positioning task depending on the camera used. Similarly to the previously described experimental protocol, the four trajectories are repeated with both cameras, and the results are presented in Table II.
<table><caption>TABLE I: APE and corresponding gain for each experiment and for both cameras. The gain is calculated as the percentage of the APE before correction to the APE after correction. **Bold** values indicate the highest gain in performance for each camera.</caption><thead><tr><th>APE</th><th>Exp.<br>Cam.</th><th>1.1</th><th>1.2</th><th>1.3</th><th>1.4</th><th>2.1</th><th>2.2</th><th>2.3</th><th>2.4</th><th>3</th><th>4.1</th><th>4.2</th><th>4.3</th><th>4.4</th></tr></thead><tbody><tr><td>Before<br>Correction</td><td>Omnidir.<br>RGB-D</td><td>0.54<br>0.34</td><td>0.72<br>0.26</td><td>0.62<br>0.20</td><td>0.54<br>0.58</td><td>0.40<br>0.51</td><td>0.44<br>0.37</td><td>0.25<br>0.33</td><td>0.47<br>0.33</td><td>0.58<br>0.59</td><td>0.12<br>0.47</td><td>0.10<br>0.28</td><td>0.30<br>0.44</td><td>0.53<br>0.81</td></tr><tr><td>After<br>Correction</td><td>Omnidir.<br>RGB-D</td><td>0.23<br>0.26</td><td>0.23<br>0.16</td><td>0.13<br>0.03</td><td>0.15<br>0.34</td><td>0.16<br>0.13</td><td>0.34<br>0.09</td><td>0.27<br>0.02</td><td>0.07<br>0.03</td><td>0.11<br>0.29</td><td>0.11<br>0.29</td><td>0.07<br>0.28</td><td>0.08<br>0.81</td><td>0.12<br>0.47</td></tr><tr><td>Gain (%)</td><td>Omnidir.<br>RGB-D</td><td><b>56.68</b><br>24.60</td><td><b>67.44</b><br>38.57</td><td>78.71<br><b>84.38</b></td><td><b>72.40</b><br>42.38</td><td>58.96<br><b>74.37</b></td><td>23.65<br><b>74.73</b></td><td>-8.61<br><b>93.64</b></td><td>85.24<br><b>92.35</b></td><td><b>80.45</b><br>50.83</td><td>8.27<br><b>37.47</b></td><td><b>30.27</b><br>-0.19</td><td><b>73.09</b><br>-82.82</td><td><b>77.11</b><br>41.85</td></tr></tbody></table>

<table><caption>TABLE II: Similarly to Table I, APE and corresponding gain for a subset of experiments for both cameras after modification of the environment. **Bold** values indicate the highest gain in performance for each camera.</caption><thead><tr><th>APE</th><th>Exp.<br>Cam.</th><th>1.2</th><th>1.4</th><th>2.2</th><th>2.4</th></tr></thead><tbody><tr><td>Before<br>Correction</td><td>Omnidir.<br>RGB-D</td><td>0.54<br>0.48</td><td>0.42<br>0.46</td><td>0.38<br>0.49</td><td>0.65<br>0.52</td></tr><tr><td>After<br>Correction</td><td>Omnidir.<br>RGB-D</td><td>0.38<br>0.45</td><td>0.18<br>0.44</td><td>0.27<br>0.55</td><td>0.23<br>0.16</td></tr><tr><td>Gain (%)</td><td>Omnidir.<br>RGB-D</td><td><b>28.71</b><br>7.61</td><td><b>56.42</b><br>3.49</td><td><b>29.06</b><br>-13.7</td><td>65.22<br><b>68.05</b></td></tr></tbody></table>

2) *Results analysis:* The results of this robustness to environmental changes analysis are presented in Table II and highlights the gain in accuracy in the positioning task of each camera used.

The omnidirectional camera consistently achieves substantial improvements across all evaluated trajectories with a significant gain ranging from 28.71% to 65.22% demonstrating robust correction performance despite environmental modifications.

In contrast, the RGB-D camera exhibits more variable behavior. While it achieves a high gain in Experiment 2.4 (68.05%), it provides only marginal improvement in the three other. These findings suggest a strong sensitivity of RGB-D-based SLAM to environmental changes, likely due to its dependence on consistent depth and appearance features that may no longer be present or reliable in the modified scenes.

Overall, the omnidirectional camera clearly outperforms the RGB-D camera in changed environments. This is particularly evident in experiments where the latter either fails to improve or worsens the trajectory estimate. While Experiment 2.4 yields similar results for both systems (with only a 2.83% difference in gain), this case appears to be especially favorable for RGB-D sensing and is not representative of general behavior across diverse trajectories (as it was already the case in the previous set of experiments presented in Table I).

In summary, the outcomes of this robustness analysis indicate that the omnidirectional camera offers a more robust

and reliable solution for pose correction in environments subject to change. Its wide FoV and stable image features enable effective re-localization even when the environment differs from the mapping conditions. In contrast, the RGB-D camera's performance degrades significantly under such changes, making it less suitable for long-term or dynamic deployments without frequent remapping.

### E. Wrap-up and discussion

The experiments conducted with the proposed *Baseline Biped Positioning* framework highlight its effectiveness in improving humanoid robot positioning accuracy across different vision modalities. The usage of both omnidirectional and RGB-D cameras provide benefits, with notable reductions in APE across all trajectory types. The RGB-D camera achieves higher peak accuracy gains in feature-rich parts of the environment, thanks to its depth sensing capabilities. However, its limited FoV and reliance on structured visual input leads to inconsistent performance, particularly in scenarios with fewer or changing visual features.

In contrast, the omnidirectional camera offers more stable and robust improvements. Despite lacking depth information, its 360° FoV enables reliable feature tracking throughout the environment, supporting consistent localization even when visual conditions vary. This advantage is especially evident in the robustness analysis, where the omnidirectional system maintains high correction performance despite environmental changes, whereas the RGB-D camera exhibits degraded or erratic behavior.

Overall, the results underline a key trade-off: while the RGB-D camera can provide precise corrections under ideal conditions, the omnidirectional camera delivers more reliable and generalizable performance. For long-term or dynamic deployments, the latter presents a more robust solution within the *Baseline Biped Positioning* framework.

## IV. CONCLUSIONS AND FUTURE WORKS

In this article, we have introduced a *Baseline Biped Positioning* system to reach goal poses with a humanoid robot based on two different vision modalities: a monocular 360° camera and a RGB-D camera coupled with a V-SLAM system. By comparing both cameras in real experiments, we have shown that while the RGB-D camera and its depth
information can provide a more accurate localization when placed in a favorable part of the environment, its lack of robustness to environmental changes limits its use in real-world applications. On the other hand, while lacking depth information, the monocular 360° camera provides a more robust localization, allowing the robot to navigate in a wider range of environments, outperforming RGB-D camera and showing that using wider FoV is beneficial over having a depth sensor.

This work opens up new perspectives for humanoid positioning and navigation, allowing the use of omnidirectional cameras that are becoming more and more common in the robotics community. Future works include the use of partial maps to set the scale factor to navigate in not yet mapped environments, usage of NeRF based approach and depth estimation methods, or using the *Baseline Biped Positioning* framework to constrain the *Baseline Walking Controller* to improve humanoid navigation.

## V. ACKNOWLEDGMENTS

The authors thank the ANR - FRANCE (French National Research Agency) for its financial support of the Ant'noid project n° ANR-24-CE33-0218-01. The authors also thank Mathis D'HAENE for his help in multiple experiments.

## REFERENCES

[1] R. Siegwart, I. R. Nourbakhsh, and D. Scaramuzza, *Introduction to Autonomous Mobile Robots*, 2nd ed. The MIT Press, 2011.

[2] V. Sushrutha Raghavan, D. Kanoulas, C. Zhou, D. G. Caldwell, and N. G. Tsagarakis, "A study on low-drift state estimation for humanoid locomotion, using lidar and kinematic-inertial data fusion," in *Proc. of IEEE-RAS Int. Conf. on Humanoid Robots*, 2018, pp. 1-8.

[3] A. Roychoudhury, S. Khorshidi, S. Agrawal, and M. Bennewitz, "Perception for humanoid robots," *Current Robotics Reports*, vol. 4, pp. 1-14, 2023.

[4] C. Dune, A. Herdt, E. Marchand, O. Stasse, P.-B. Wieber, and E. Yoshida, "Vision based control for Humanoid robots," in *IEEE/RSJ Int. Conf. on Intelligent Robots and Systems' workshop on Visual Control of Mobile Robots (ViCoMoR)*, 2011, pp. 19-26.

[5] J. Delfín, H. M. Becerra, and G. Arechavaleta, "Visual path following using a sequence of target images and smooth robot velocities for humanoid navigation," in *Proc. of IEEE-RAS Int. Conf. on Humanoid Robots*, 2014, pp. 354-359.

[6] O. Stasse, "SLAM and Vision-based Humanoid Navigation," in *Humanoid Robotics: A Reference*, 2018, pp. 1739-1761.

[7] K. Chappellet, M. Murooka, G. Caron, F. Kanehiro, and A. Kheddar, "Humanoid loco-manipulations using combined fast dense 3D tracking and SLAM with wide-angle depth-images," *IEEE Trans. on Automation Science and Engineering*, 2024.

[8] J. Ducrocq and G. Caron, "A Survey on Adaptive Cameras," *Int. J. of Computer Vision*, vol. 132, pp. 2989-3022, 2024.

[9] R. Mur-Artal and J. D. Tardós, "ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras," *IEEE Trans. on Robotics*, vol. 33, no. 5, pp. 1255-1262, 2017.

[10] A. Tanguy, D. De Simone, A. I. Comport, G. Oriolo, and A. Kheddar, "Closed-loop MPC with Dense Visual SLAM - Stability through Reactive Stepping," in *Proc. of IEEE Int. Conf. on Robotics and Automation*, 2019, pp. 1397-1403.

[11] S. Sumikura, M. Shibuya, and K. Sakurada, "OpenVSLAM: A Versatile Visual SLAM Framework," in *Proc. of ACM Int. Conf. on Multimedia*, 2019, pp. 2292-2295.

[12] S. Schulte, A. N. André, N. Crombez, and G. Caron, "On the impact of the camera field-of-view to direct visual servoing robot trajectories when using the photometric gaussian mixtures as dense feature," in *Proc. of IEEE/SICE Int. Symp. on Systems Integration*, 2025, pp. 1-6.

[13] K. Chappellet, G. Caron, F. Kanehiro, K. Sakurada, and A. Kheddar, "Benchmarking cameras for OpenVSLAM indoors," in *Proc. of Int. Conf. on Pattern Recognition*, 2021, pp. 4857-4864.

[14] Y. Omori, T. Furukawa, T. Ishikawa, and M. Inaba, "Humanoid Vision Design for Object Detection, Localization and Mapping in Indoor Environments," in *Proc. of IEEE Int. Symp. on Safety, Security, and Rescue Robotics*, 2018, pp. 1-6.

[15] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, "RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments," in *Proc. of Int. Symp. on Experimental Robotics*, 2014, pp. 477-491.

[16] C. Audras, A. Comport, M. Meilland, and P. Rives, "Real-time dense appearance-based SLAM for RGB-D sensors," in *Proc. of Australasian Conf. on Robotics and Automation*, vol. 2, 2011, p. 2.

[17] S. Zhang, L. Zheng, and W. Tao, "Survey and evaluation of RGB-D SLAM," *IEEE Access*, vol. 9, pp. 21 367-21 387, 2021.

[18] R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon, "Kinectfusion: Real-time dense surface mapping and tracking," in *Proc. of IEEE Int. Symp. on Mixed and Augmented Reality*, 2011, pp. 127-136.

[19] T. Whelan, S. Leutenegger, R. F. Salas-Moreno, B. Glocker, and A. J. Davison, "ElasticFusion: Dense SLAM without a pose graph," in *Proc. of Robotics: Science and Systems*, vol. 11, 2015, p. 3.

[20] A. Rosinol, J. J. Leonard, and L. Carlone, "NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields," in *2023 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)*, 2023, pp. 3437-3444.

[21] K. Yang, Y. Cheng, Z. Chen, and J. Wang, "SLAM Meets NeRF: A Survey of Implicit SLAM Methods," *World Electric Vehicle Journal*, vol. 15, no. 3, 2024.

[22] T. Deng, G. Shen, C. Xun, S. Yuan, T. Jin, H. Shen, Y. Wang, J. Wang, H. Wang, D. Wang, et al., "MNE-SLAM: Multi-Agent Neural SLAM for Mobile Robots," in *Proceedings of the Computer Vision and Pattern Recognition Conf. (CVPR)*, Nashville, USA, 2025, pp. 1485-1494.

[23] S. Li, "Full-view spherical image camera," in *Proc. of Int. Conf. on Pattern Recognition*, vol. 4, 2006, pp. 386-390.

[24] G. Caron and F. Morbidi, "Spherical Visual Gyroscope for Autonomous Robots using the Mixture of Photometric Potentials," in *Proc. of IEEE Int. Conf. on Robotics and Automation*, 2018, pp. 820-827.

[25] J. Li, X. Wang, and S. Li, "Spherical-model-based SLAM on full-view images for indoor environments," *Applied Sciences*, vol. 8, no. 11, p. 2268, 2018.

[26] Y. Zhang and F. Huang, "Panoramic visual SLAM technology for spherical images," *Sensors*, vol. 21, no. 3, p. 705, 2021.

[27] Q. Chang, Q. Liu, X. Yang, H. Yajiang, F. Ren, and Y. Cui, "The relocalization of SLAM tracking based on spherical cameras," *IEEE Access*, vol. 9, pp. 159 764-159 783, 2021.

[28] R. Scona, S. Nobili, Y. R. Petillot, and M. Fallon, "Direct visual SLAM fusing proprioception for a humanoid robot," in *Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems*, 2017, pp. 1419-1426.

[29] A. Kheddar, S. Caron, P. Gergondet, A. Comport, A. Tanguy, C. Ott, B. Henze, G. Mesesan, J. Englberger, M. Roa, P.-B. Wieber, F. Chaumette, F. Spindler, G. Oriolo, L. Lanari, A. Escande, K. Chappellet, F. Kanehiro, and P. Rabate, "Humanoid robots in aircraft manufacturing: the Airbus use-cases," *IEEE Robotics & Automation Magazine*, vol. 26, no. 4, pp. 30-45, 2019.

[30] M. Murooka, M. Morisawa, and F. Kanehiro, "Centroidal trajectory generation and stabilization based on preview control for humanoid multi-contact motion," *IEEE Robotics and Automation Letters*, vol. 7, no. 3, pp. 8225-8232, 2022.

[31] M. Grupp, "evo: Python package for the evaluation of odometry and SLAM," https://github.com/MichaelGrupp/evo, 2017.