# Improving Landmark Positions in Urban Maps with Crowdsourced Data

Noël Nadal, Jean-Marc Lasgouttes, Fawzi Nashashibi

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To cite this version:

Noël Nadal, Jean-Marc Lasgouttes, Fawzi Nashashibi. Improving Landmark Positions in Urban Maps with Crowdsourced Data. ITSC 2025 - IEEE International Conference on Intelligent Transportation Systems, Nov 2025, Gold Coast, Australia. pp.8. hal-05170266

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# Improving Landmark Positions in Urban Maps with Crowdsourced Data

Noël Nadal<fnref n="1" />, Jean-Marc Lasgouttes<fnref n="2" /> and Fawzi Nashashibi<fnref n="3" />

**Abstract**—Precise localization is critical for the safety and efficiency of autonomous driving, where reliable navigation and decision-making depend on accurate geospatial data. A well-established technique to achieve decimeter-level precision is the use of landmarks, which requires highly accurate and frequently updated maps to ensure optimal performance. Although prior research has extensively explored the addition or removal of landmarks in maps, improving the positional accuracy of existing landmarks has been less addressed, with existing solutions often limited to validating landmark presence or absence. This study proposes a novel approach to refine the coordinates of existing landmarks using crowdsourced data, collected from sensors that may include potentially low-cost, low-precision devices. Our results demonstrate that even a few crowdsourced updates can significantly enhance landmark accuracy. These findings highlight the scalability and cost-effectiveness of crowdsourcing for map refinement, opening promising perspectives for low-cost, high-precision map updates in autonomous driving and other geospatial applications.

## I. INTRODUCTION

Accurate localization is a cornerstone of autonomous driving, enabling vehicles to navigate complex environments with precision and safety [1], [2]. Achieving reliable localization requires determining the position of a vehicle with high accuracy, often at the decimeter level, to support critical tasks such as path planning, obstacle avoidance, and decision-making. One well-established approach for achieving this level of precision is map-based localization [3]. The success of this approach depends on the quality of the map [4], particularly the accuracy of its data, as even minor errors can lead to significant localization failures, compromising the safety and efficiency of autonomous systems.

The importance of maintaining accurate and up-to-date maps cannot be overstated. For landmark-based localization to achieve decimeter-level accuracy, maps must reflect the real-world environment with high fidelity, capturing the exact positions of landmarks and adapting to changes over time. However, creating and maintaining such maps poses significant challenges [5].

In parallel, crowdsourcing has emerged as a powerful paradigm for collecting and processing large-scale geospatial data. Using data from a diverse range of contributors, such as satellite images [6] or vehicle sensors [7], crowdsourcing enables the continuous updating of maps at a fraction of the cost of traditional methods. Recent advances in crowdsourcing have demonstrated its potential for map creation and maintenance, particularly in dynamic environments.

Recent work on map improvement has focused mainly on road lane updates [8]. However, map-based localization may also work by detecting and matching known landmarks – such as road signs, poles, or other fixed features – with known positions that are stored in a map [9]. Current management techniques often focus on adding new landmarks or removing obsolete ones as environments evolve [10], [11], but the task of refining the positional accuracy of existing landmarks has received less attention. Such solutions prioritize validating the presence or absence of landmarks, leaving their precise coordinates potentially under-optimized, which limits the potential for high-precision localization. Other solutions improve landmarks issued from Simultaneous Localization and Mapping (SLAM) techniques [12], [13], which lead to the usual limitations of SLAM approaches in terms of localization accuracy, robustness in outdoor environments and real-time performance [14].

This paper addresses these challenges by proposing a novel method to enhance the positional accuracy of landmarks on existing maps using crowdsourced data. Our approach integrates location updates from various sources, including potentially low-cost sensors, to perform perception fusion [15], [16] and iteratively refine landmark coordinates. With the help of our previous work on landmark geopositioning [17], we demonstrate that even a few crowdsourced updates can significantly improve map accuracy, offering a scalable and cost-effective solution to improve high-precision map maintenance. We detail two approaches which both have very few constraints on the input data, meaning that it is compatible with many other geopositioning algorithms.

The remainder of this document is organized as follows. Section II details our two proposed approaches for improving known landmarks. Section III presents our custom simulator, our experiments, and their results, as well as some analysis. Finally, Section IV describes future research directions.

## II. METHODOLOGY

Fig. 1 summarizes the general idea of our approach. On the one hand, one or more vehicles drive through an urban area and attempt to geolocate themselves using a (potentially inaccurate) landmark map. During this process, vehicles match their observations to landmarks stored on this map and, once the vehicle’s position has been estimated, determine estimates of the position of the corresponding landmarks, all of which are sent in a single batch to a server. On the other hand, this server collects all the landmark position estimates from the map and, off-line and at regular intervals, refines the map using these estimates.

<footnote n="1">¹Inria, France noel.nadal@inria.fr</footnote>

<footnote n="2">²Inria, France jean-marc.lasgouttes@inria.fr</footnote>

<footnote n="3">³Inria, France fawzi.nashashibi@inria.fr</footnote>
<figure><img src="image_3.png" /><figcaption>One or several vehicles Centralized server</figcaption></figure>

Fig. 1: Diagram of our method. Several vehicles compute landmark estimates while driving (left side), sending landmark position estimates to our centralized server performing offline updates (right side). The server then sends back the updated map to vehicles for their next ride.

The map is defined as a list of N landmarks. Such a map should contain at least the coordinates of each landmark. However, since we are dealing with potentially inaccurate maps, one may want to have an estimation of the error on each of these coordinates. Therefore, another way to define the map is to describe it as a list of random variables given by coordinates along with a covariance matrix.

The data retrieved by the server are as follows: for each geopositioning attempt of a given vehicle, this vehicle sends the list of estimated positions for the landmarks it has matched to its local map. Each estimate should come with the following information: the identifier of the landmark on the map and its coordinates according to the vehicle. Additionally, the landmark's position may be modeled as a random variable; in this case, the aforementioned coordinates correspond to the mean of the variable, and its 2 × 2 covariance matrix is also provided. The server will then need to update the map using the data sent by all the vehicles.

In the remainder of this section, we will describe two distinct approaches to applying this idea: one that computes estimates without taking into account the previous map's estimates, and another one that performs Bayesian fusions to update the map. For a given landmark $\mathbf{p}_i$, we denote $L_i$ the number of estimates for this landmark, and $L$ the maximum over all $L_i$ values. In both approaches, we assume that the different estimates of the same landmark are independent; this is possible using our geopositioning algorithm, provided the estimations are computed based on the estimation fusion.

### A. Approach A: memoryless updates

In this approach, for each landmark $\mathbf{p}_i$ on the map, we have $L_i$ estimates $(p_{ik}^x, p_{ik}^y)$ of that landmark's position. To

determine our new estimate $(\bar{p}_i^x, \bar{p}_i^y)$, we simply compute the empirical mean of these estimates.

$$ \bar{p}_i^x = \frac{1}{L_i} \sum_{k=1}^{L_i} p_{ik}^x \quad \text{and} \quad \bar{p}_i^y = \frac{1}{L_i} \sum_{k=1}^{L_i} p_{ik}^y \qquad (1) $$

Each landmark update requires $\bar{O}(L_i)$ time, leading to a $\bar{O}(LN)$ total update time. We do not take the previous map's estimate of the landmark into account. Indeed, the newer estimates have been determined based on a newer map, which is most likely better, as one more crowdsourced update has been performed. The resulting estimate may be very inaccurate if the number of observations made of a given landmark is too low. For this reason, at each iteration only landmarks that were observed and matched at least 10 times are updated.

This approach relies only on the estimated coordinates of the landmarks, making it compatible with most geolocation algorithms.

### B. Approach B: Bayesian fusion updates

In this approach, we have $2\ell$ estimates ($\ell$ for each coordinate), including the estimates of the coordinates of the landmark on the previous map, together with the corresponding covariance matrix $\Sigma_\ell$, and $A_\ell = \Sigma_\ell^{-1}$ the corresponding precision matrix (both square matrices of size $2\ell \times 2\ell$). We then perform a Bayesian fusion of these estimates to determine the new estimate of the landmark.

Formally, we consider a vector $\mathbf{z}$ of estimates of size $2\ell$, the first $\ell$ coordinates corresponding to the estimates of the $\mathbf{x}$ coordinate and the $\ell$ others to estimates of the $\mathbf{y}$ coordinate. Let, $\mathbf{e} = (1, \dots, 1)^T$ a vector of size $\ell$ and $\mathbf{E}$ of size $2 \times 2\ell$ defined as

$$ \mathbf{E} \stackrel{\text{def}}{=} \begin{pmatrix} \mathbf{e} & \mathbf{0} \\ \mathbf{0} & \mathbf{e} \end{pmatrix}. \qquad (2) $$

The estimated position of the landmark is $\mathbf{p}_i$, then using the formulas described in [17] we find the following

$$ \mathbf{p}_i = \mathbf{M}\mathbf{z}_i, \text{ with } \mathbf{M} = (\mathbf{E}^T \mathbf{A}_\ell \mathbf{E})^{-1} \mathbf{E}^T \mathbf{A}_\ell. \qquad (3) $$

The covariance matrix of $\mathbf{p}_i$ is then given by

$$ \Sigma_{\mathbf{p}_i} = \mathbf{M}^T \Sigma_{\ell} \mathbf{M}. \qquad (4) $$

As some estimates are computed using the same vehicle position estimation, there is some dependence between estimations of distinct landmarks. If $\mathbf{p}_i \stackrel{\text{def}}{=} \mathbf{M}_i \mathbf{z}_i$ and $\mathbf{p}_j \stackrel{\text{def}}{=} \mathbf{M}_j \mathbf{z}_j$ are two landmark position estimations, then their covariance is

$$ \text{Cov}(\mathbf{p}_i, \mathbf{p}_j) = \mathbf{M}_i \text{Cov}(\mathbf{z}_i, \mathbf{z}_j) \mathbf{M}_j^T. \qquad (5) $$

Let $\text{Cov}(\mathbf{z}_i, \mathbf{z}_j) = (b_{kl}^{ij})$. If the estimate $k$ of $\mathbf{z}_i$ and the estimate $l$ of $\mathbf{z}_j$ were determined based on the same vehicle estimate $\mathbf{v} = (v_x, v_y)$ and both correspond to the same coordinate $(x$ or $y), then $b_{kl}^{ij}$ is the variance of the corresponding coordinate of $\mathbf{v}$: $b_{kl}^{ij} = \text{Var}(v_x)$ or $b_{kl}^{ij} = \text{Var}(v_y)$. If they were determined based on the same vehicle estimate but do not correspond to the same coordinate, then $b_{kl}^{ij}$ is the covariance between the coordinates of $\mathbf{v}$: $b_{kl}^{ij} = \text{Cov}(v_x, v_y)$. Else, $b_{kl}^{ij} = 0$.
This lets us compute the covariance between the landmarks $\mathbf{p}_i$ and $\mathbf{p}_j$.

Although a fusion of $L_i$ estimates takes $\mathcal{O}(L_i^3)$ in the general case, here all estimates of a given landmark are assumed to be independent. Therefore, all covariances except the covariances between the coordinates of the same estimate are equal to 0. For this reason, it is possible to split these estimates into groups of 10 estimates, for instance, and then keep fusing the resulting estimates until we compute our final estimate. This can be done in $\mathcal{O}(L_i)$ time, so that the map update can be done in $\mathcal{O}(LN)$ time. Similarly, while computing a covariance between two landmarks that have been respectively estimated $L_i$ and $L_j$ times may take $\mathcal{O}((L_i+L_j)^2)$ time, since many values equal null, we end up summing $\mathcal{O}(L_i+L_j)$ values for each covariance, meaning that the covariance matrix for the map can be updated in $\mathcal{O}(LN^2)$. Furthermore, the number of covariances that are updated is the number of pairs of landmarks observed at once, which is much lower than $N^2$ in practice. In fact, for each vehicle position, the number of landmarks that can be observed at the same time is bounded by the distance at which vehicles can detect landmarks and the density of the landmarks on the map, which may both be bounded by a constant in practice, leading again to a $\mathcal{O}(LN)$ complexity.

This approach, unlike the previous one, takes the previous map's estimate of the landmark into account. Although this estimate may be of lesser quality, this is reflected in its covariance, which is available on the map before the update.

### III. EXPERIMENTATION

#### A. Simulator

These two approaches were tested using our custom simulator, implemented in C++ using RTMaps software [18]. The idea is to have one vehicle move on a local map at a constant speed to simulate the crowdsourcing part. This local map is based on an OpenStreetMap export of the 12th arrondissement of Paris. We retrieve information on the streets and their nodes to create an oriented graph on which the vehicle may move. We randomly generate landmarks on the side of the edges of the graph. We then apply a Gaussian-centered error with a standard deviation of 10 cm, 20 cm or 40 cm depending on the experiment. The resulting coordinates are the initial estimated positions of our landmarks. Our simulator uses the real coordinates of the landmarks and not these ones to simulate landmark detection, hence accounting for the inaccuracy of our map.

We perform several iterations of our approaches. For each iteration, we perform a one hour ride on our map and retrieve position estimations of landmarks that have been successfully observed and matched to a known landmark of the map by our algorithm [17]. In all our experiments, we generate 4000 landmarks, split into two types (for example, 2000 traffic lights and 2000 traffic signs). For this purpose, the simulator described in [17] has been updated to handle more than one landmark type and is now able to match up to 8 landmarks per 40 ms, which leads to a maximum number of 720000 landmark estimations per iteration (though in practice we

<table><caption>TABLE I: Numerical values used for localization simulation</caption><tr><td>σ<sub>u</sub></td><td>0.056 m/s</td><td>vehicle speed sensor error</td></tr><tr><td>σ<sub>Δθ</sub></td><td>0.0044 rad</td><td>vehicle angle sensor error</td></tr><tr><td>σ<sub>d</sub></td><td>0.023 m</td><td>landmark distance error</td></tr><tr><td>σ<sub>α</sub></td><td>0.00017 rad</td><td>landmark angle error</td></tr><tr><td>σ<sub>i</sub></td><td>0.1 m</td><td>error initial vehicle position</td></tr><tr><td>σ<sub>m</sub></td><td>0.1 m</td><td>error initial landmark map</td></tr></table>

would never reach that upper bound). We assume accurate semantic landmark matching for the sake of this experiment. Some numerical constants are listed in Table I.

After each simulation, we run our crowdsourcing algorithm to update the landmarks that have been observed enough times, and merge these landmarks with the known landmarks that have not been observed this time. This leads to our new map, which is then used for the next iteration. Each experiment consists of 10 such iterations.

In the specific case of approach A, since our localization algorithm requires a covariance matrix for our map and this approach does not provide a correct one, we computed the empirical variances of the observations for each updated landmark and kept all covariances at 0.

#### B. Results

As mentioned earlier, we conducted six experiments in total: three with approach A, three with approach B, with an initial standard deviation for the map error of 10 cm, 20 cm and 40 cm, respectively. Each map update took between 33.36 and 35.35 seconds to process with approach A and between 44.2 and 46.07 seconds with approach B. This means that the execution time would be acceptable even with a larger amount of data collected before each iteration, since we are performing offline updates. In both approaches, each iteration was slightly longer to compute than the previous one, as the number of nonzero covariances also increased.

For each approach, we compared the distance between the real and estimated positions of the landmarks, before and after iterations 1, 5, and 10. Fig. 2 shows the results for approach A with a 10 cm initial error, while Fig. 3 shows similar results for approach B. In both approaches, the result patterns remained similar for larger initial errors.

Next, Fig. 4 details the evolution of the average error, this time weighting each landmark by the number of times it was successfully observed during the iteration. This weighting allows each landmark to have an importance proportional to the frequency with which it is used to geolocate a vehicle.

For each experiment, we have tried to analyze the evolution of the distribution of landmark positioning errors: to illustrate this, Fig. 5 describes the evolution of the deciles of these errors, after iteration 5, for both approaches.

#### C. Analysis

Overall, the results are very encouraging: the vast majority of updated landmarks see their error decrease in the first iterations. As the iterations progress, a growing number of landmarks have an error between 0 and 5 centimeters, and
<figure><img src="image_4.png" /><figcaption>(a) Iteration 1</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(a) Iteration 1</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(b) Iteration 5</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(b) Iteration 5</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(c) Iteration 10</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(c) Iteration 10</figcaption></figure>

Fig. 2: Approach A (memoryless updates). Comparison of errors on landmarks before and after (a) Iteration 1, (b) Iteration 5 and (c) Iteration 10, when the initial error on the map is 10 cm. A landmark's estimate is improved if and only if its dot (in blue) is below the y = x line (in black).

Fig. 3: Approach B (Bayesian fusion). Comparison of errors on landmarks before and after (a) Iteration 1, (b) Iteration 5 and (c) Iteration 10, when the initial error on the map is 10 cm. A landmark's estimate is improved if and only if its dot (in blue) is below the y = x line (in black).
<figure><img src="image_10.png" /><figcaption>Fig. 4: Evolution of the mean error weighted by landmark observation frequency.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 5: Evolution of map landmark error by deciles, for iteration 5 only.</figcaption></figure>

the proportion of landmarks whose localization is improved tends towards 50%. The reason for this is that the landmarks are not perfectly detected by the vehicles, and this residual error reflects the errors in the sensors used. Therefore, once a landmark's error is below the sensor's, its error evolves randomly and has a (roughly) 50% probability of improving. Although this error is unlikely to be eliminated (unless the quality of the sensors is improved), it is not a problem, since the aim is to geolocate with a decimetric error, and using landmarks known with 5 cm accuracy is thus sufficient for this purpose.

In the case of experiments with an initial error of 20 cm or 40 cm, the results are similar, although convergence is slower in the sense that the number of iterations required to obtain a similar result is larger. The long-term accuracy of the map ultimately depends primarily on the errors in landmark detection. This leaves us optimistic that, no matter how inaccurate a newly added landmark may be, as long as the

available data allow vehicles to match it to their observations, it will eventually be accurately estimated.

Although the landmark detection error is the same from start to finish, it is logical that several iterations are required to improve the landmark position as much as possible. In fact, when these positions are estimated before being sent, the estimate is subject to both sensor error and error on the estimated position of the vehicle itself. As the initial error on the map is non-negligible, in the first iterations the vehicle does not geolocalize as well as in previous iterations, resulting in additional error. As iterations progress, a kind of virtuous circle enables the vehicle to not only geolocate more accurately but also send more accurate data. In addition, since our vehicle moves randomly on the map, the number of distinct landmarks detected ranged from 1400 to 2500 (out of 4000 in total), depending on the iteration.

However, it seems that convergence is slower for approach B (Bayesian fusion) than for approach A (memoryless updates). A major difference between these two approaches, as a reminder, is that the estimate from the previous iteration is taken into account when updating the estimate. Although the covariance of the different estimates should enable us to refine the weight of each data point, it would seem that the addition of this old estimate had a negative impact on the updating of certain landmarks, particularly those that had not been observed often enough (for example, around 10, which was the minimum not to be ignored). More specifically, we observe an effect of overconfidence in the previous estimate; consequently, it would be interesting to re-test approach B without taking into account the previous estimate. This difference is especially visible when weighting landmarks by their number of observations.

Some landmarks, while having a very low error in the previous iteration, suddenly find themselves with a large error after being updated (these are the points on the left above the diagonal on the graphs). After verification, it would appear that imprecise vehicle localization, coupled with a relatively small number of observations, can lead to such an increase in error. While this problem appears to be fairly rare over the long term with approach A, it is more frequent with approach B, not only because the lower accuracy of the map increases the chances that the vehicle's position is known with less precision, but also because we did not apply the minimum of 10 observations to update a landmark.

Additionally, some other landmarks, although observed and matched, retain a sizable error that does not diminish. These points are close to the diagonal line on the figures. After looking into that issue, we noticed that certain isolated areas of the map concentrate a number of landmarks that are very inaccurately referenced in the map, preventing the vehicle observing them from locating itself correctly and thus providing better estimates. The virtuous circle mentioned above is not triggered, so that it takes much longer to converge such landmarks. In an extra experiment with 40 iterations, we observed that, although such landmarks continued to appear for many iterations, their number was decreasing. This suggests a slow convergence requiring many vehicles
visiting these isolated areas. Again, this problem is more
blatant with approach B; as noted in the previous paragraph,
the reduced accuracy of landmarks leads to increased vehicle
position errors.

Last but not least, some landmarks are never matched to
an observation and therefore never updated. This may be
because the vehicle never observed it, or because the initial
error was too large relative to the variance initially applied.
For example, we can observe this for Approach A with an
initial error of 20 cm or 40 cm, since the landmark with the
highest initial error does not improve over time. Such a
situation would be improved if the addition and deletion of
landmarks were managed. Indeed, our initial landmark in the
map would eventually be deleted, and the observations of this
landmark, unmatched, would cause a new landmark to be
added as a replacement. In this way, the problem would be
solved, again with a longer-than-average convergence time.

IV. CONCLUSION

In this paper, we studied two methods to improve the
positional accuracy of the landmarks in existing maps
by leveraging crowdsourced data, based on our previous
work in landmark geopositioning [17]. Our two approaches
complement each other, offering broad compatibility with
various geopositioning algorithms, with or without covariance
data for our map, enhancing their applicability in real-
world scenarios. The results validate the scalability and cost-
effectiveness of our solution, providing a robust framework
for maintaining high-precision maps. Although our results
are highly dependent on our own geopositioning algorithm,
using better localization methods in place of ours should even
improve the results; therefore, this should be interpreted as a
lower bound on the efficiency of the approach.

As mentioned earlier, our future work will include ex-
perimenting with the second approach without taking into
account the previous map's estimates, as our choice to reuse
previous results led to some overconfidence. Additionally, for
our localization system to be complete, we would need to add
a landmark management approach that manages the addition
and removal of landmarks. Future work will thus involve
integrating such an algorithm for full testing as well, either in
our simulator or under real-life conditions. Last but not least,
our aim is to be able to present theoretical results proving
the improvement of the map via the approaches presented.

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