Sensible techniques to support smartphone location accuracy


The Google Smartphone Decimeter Problem (SDC) pageant, co-sponsored by way of the Institute of Navigation (ION), came about this summer time. For the contest, groups advanced high-precision GNSS positioning the usage of a pool of smartphone GNSS + inertial size unit (IMU) datasets accompanied by way of high-accuracy floor fact. Groups competed to reach the most efficient location accuracy with the datasets supplied. Winners won money prizes and backed attendance on the ION GNSS+ 2022 convention in Denver, Sept. 19-23, to give their effects.


The SDC has its origins within the Android Working Gadget, which is an open-source platform. In 2016, Google made GNSS uncooked measurements to be had as a public utility programming interface (API) on all Android telephones. Since then, the to be had measurements have turn into extra refined and extra correct. As an example, dual-frequency carrier-phase information is now to be had on many Android telephones. This allows new spaces of analysis.


The contest had two targets:
• Stimulate the analysis and construction of high-accuracy algorithms that may produce submeter place accuracy on telephones.

• Determine a publicly out there repository of classified information so that every one long run analysis on location algorithms will also be judged in a constant manner towards a regular set of information.

The primary objective used to be met past our expectancies. A complete of one,381 groups participated within the two competitions of 2021 and 2022. Dialogue amongst competition at the pageant platform ( used to be wide-ranging, extremely collegial, and recommended to all the group.

Competition have written and shared detailed descriptions, and those had been reviewed and commented on by way of different competition. Additionally, winners have written officially peer-reviewed papers and made displays on the ION GNSS+ meetings, that are to be had from

The second one objective is a work-in-progress and is meant to be the legacy of the occasions.


Disciplines comparable to gadget finding out have established benchmarks that make it conceivable to match new approaches to earlier ones in a right kind quantitative manner. Within the GNSS group, this conference has been lacking — a look throughout papers at meetings will display that other algorithms have a tendency to be introduced with other take a look at information and other metrics. In most cases, the authors gather this knowledge, and it’s continuously reasonably sparse (one or two pressure assessments, as an example). Additionally, the reader by no means is aware of whether or not the information used to be cherry-picked (have been dangerous effects now not discussed?).

The SDC information supplies:

• 206 other pressure assessments
•86 overall hours of dual-frequency (L1, L5) information with code and carrier-phase measurements
•All classified with ground-truth positions and velocities amassed the usage of NovAtel SPAN ISA-100C, with exact lever-arm reimbursement and validated with Google’s research equipment.

The Kaggle web site allowed customers to post their effects, then routinely scored them towards the ground-truth information. We recommend that every one GNSS researchers use this useful resource to measure their location set of rules enhancements in a regular manner. This creates believe in revealed effects, accelerating the popularity and adoption of actually nice enhancements for the good thing about all the trade and GNSS customers international.

Learn methods to use the SDC information in Kaggle to check place algorithms right here.

Winners Divulge Their Approaches

The highest 3 winners of this 12 months’s Smartphone Decimeter Problem described their tasks to Matteo Luccio, GPS Global editor-in-chief.


Taro Suzuki, Chiba Institute of Generation

1st Position Winner: Two-Step Optimization of Pace and Place the usage of Smartphone’s Service Segment Observations

What’s your analysis center of attention and the way does it relate to the competition?

My present analysis specializes in the correct positioning of cars and cell robots in city environments the place GNSS multipath happens. I most often use industrial GNSS receivers for my analysis. This pageant may be very related to my present analysis, with the exception of that the smartphone is changing a receiver.

How lengthy have you ever been creating the era or way you used to win the competition?

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The contest used to be held for 3 months, however I concentrated my efforts at the previous 3 weeks. Alternatively, I used applied sciences and sources advanced in my earlier analysis (as an example, supply code advanced in remaining 12 months’s pageant).

Have you ever participated in earlier editions of this contest?

Sure, I participated within the remaining pageant and received. The way used on this 12 months’s pageant is according to the process used to win remaining 12 months’s pageant, with further inventions and enhancements.

The place, in what GNSS sign stipulations, and at what speeds have been the take a look at information amassed?

The contest supplies a coaching dataset, which incorporates uncooked GNSS observations from a smartphone put in on a automobile because it travels on genuine roads. Along with GNSS observations, the learning dataset incorporates the floor fact of the smartphone’s place. The educational dataset comprises quite a lot of GNSS sign stipulations, comparable to riding on highways round San Francisco and Los Angeles, riding on tree-lined city streets, and riding in tunnels and beneath overpasses. I’ve advanced an set of rules that makes use of a coaching dataset containing floor fact to appropriately estimate the site of smartphones in quite a lot of GNSS sign reception environments.

What accuracies have been you in a position to acquire?

The contest metric used to be “moderate of fiftieth and ninety fifth percentile horizontal mistakes.” The metrics are computed for every of the 36 runs within the take a look at dataset, that are divided into private and non-private teams, then the metrics are averaged in every staff to compute the general rating. My ultimate rating used to be 1.382 m for public and 1.229 m for personal. The most efficient rating given after the contest used to be 1.372 m for public and 1.197 m for personal. The overall end result accomplished sub-meter accuracy within the median (fiftieth percentile).

What are the important thing options of your way?

The important thing level of my means is international optimization the usage of graph optimization, not like a traditional Kalman clear out or least-squares-based positioning strategies. As well as, extremely correct relative place estimation the usage of the time distinction of service wave stages of smartphones contributed to the accuracy. Since the pageant dataset incorporated environments comparable to tunnels and increased buildings wherein GNSS can’t be won in any respect, I devised an set of rules with two optimization steps (first speed optimization, then place optimization) and carried out it to the contest. This system allows extremely correct place estimation for automobile riding information in quite a lot of GNSS sign reception environments the usage of best smartphone GNSS statement information.

What end-user packages are you anticipating your way to allow?

Decimeter-accurate location estimation may just result in lane-level navigation for cars, pedestrian navigation, and complex location-based smartphone video games.



Shubin Dai, Kaggle Neighborhood

2d Position Winner: Bettering Smartphone GNSS positioning the usage of Gradient Descent Approach

What’s your analysis center of attention and the way does it relate to the competition?

I’m an information scientist and probably the most most sensible pageant grandmasters on Kaggle. My analysis pursuits come with pc imaginative and prescient, herbal language processing, self reliant riding, and reinforcement finding out. I positioned within the most sensible 3 in 14 similar competitions (13 of which have been solo). So, regardless of my loss of background wisdom within the GNSS box, those strategies, abilities and reports helped me discover a answer.

How lengthy have you ever been creating the era or way you used to win the competition?

I spent about 50 days in this pageant, together with finding out ideas of GNSS and figuring out a wide variety of algorithms by way of studying books, papers and supply codes. The Kaggle platform may be very useful after we need to get began in a brand new box.

Have you ever participated in earlier editions of this contest?

I didn’t take part within the pageant held remaining 12 months, however I discovered so much from answers of latest years, in particular the third-place answer.

The place, in what GNSS sign stipulations, and at what speeds have been the take a look at information amassed?

The benchmark datasets come with uncooked GNSS size and uncooked readings from inertial sensors, the usage of smartphones (Xiaomi Mi 8, Google Pixel 4, and so on.) enabled with dual-frequency and ADR (accrued delta vary) in riding eventualities, amassed within the San Francisco Bay house.

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Within the GSDC2021 dataset, there are 29 drives with 73 telephone GNSS logs within the coaching set and 19 drives with 48 telephone logs within the take a look at set. In comparison to 2021’s pageant, within the GSDC2022 dataset we will see extra information total and a greater variety of routes: 62 drives with 170 telephone logs are supplied within the coaching set and 36 drives with just one telephone according to pressure are supplied within the take a look at set.

The drives within the coaching set took 15 to 60 mins at a median pace of 18 m/s.

What accuracies have been you in a position to acquire?

In keeping with the metric of this pageant, the rating is calculated because the imply of the fiftieth and ninety fifth percentile distance mistakes. The rating on my native validation set is 1.929 m, the rating at the public take a look at set is 1.608 m, and the rating on personal take a look at set is 1.499 m. After we calculate the imply error, the rating is 1.401 m on a validated set, the imply error of 40% of the journeys are beneath 1 m. I believe the contest metric is extra dependable because the ninety fifth percentile distance error could also be essential.

Via the best way, my native validation set is more challenging to optimize than the take a look at set, so the imply error at the take a look at set is predicted to be not up to 1.401 m.

What are the important thing options of your way?

The contest information is noisy because of multipath results, non-line-of-sight receptions, receiver noise and lacking information, subsequently it’s relatively difficult. I discovered that the optimum estimation for every level in the neighborhood isn’t strong and will also be suffering from noise at that time at the observe. If we will discover a answer to an entire observe globally, the noise will also be lowered because the fashion should observe a wide variety of constraints, comparable to geometry constraints, pace constrains, and international acceleration constraints.

Despite the fact that lets prolong the WLS and Kalman clear out option to take extra issues on a observe under consideration, it’s now not really easy to fashion a wide variety of constrains. However, if we use a world optimization means, comparable to issue graph optimization and neural networks, we will upload the constrains simply, which makes it extra environment friendly to habits experiments.

Following the answer of the third-place winner in remaining 12 months’s pageant, I used the worldwide optimization means by way of making an allowance for gradient descent, pseudorange, pseudorange price, accrued service part (ADR), telephone pace and acceleration constraints of each and every time epoch on a observe. When optimizing the observe the usage of gradient descent, the losses are designed to clear out strange information and scale back the noise by way of a chain of bodily and geometrical laws. I spent a lot time on the lookout for the restrictions, proving them and turning them into losses that can be utilized to replace the coordinates iteratively all through the contest.

What end-user packages are you anticipating your way to allow?

In keeping with the environment of this pageant, we will post-process information amassed the usage of Android telephones, which is well got. The observe got can then be optimized the usage of the answers from this pageant. The answers from the primary and the second one position can each be regarded as as a framework that may be prolonged by way of including extra constrains to it to support accuracy.



Tim Everett, RTK Experts LLC

third Position Winner: An RTKLIB Open-Supply-Based totally Resolution

What’s your analysis center of attention and the way does it relate to the competition?

I expand and take care of the demo5 fork of the preferred RTKLIB open-source GPS/GNSS device device. I’ve optimized this device for low cost precision GNSS answers, so it is extremely intently associated with the targets of this pageant. My background is in regulate machine idea and I labored in product and era construction for servo techniques within the disk pressure trade for 25 years ahead of switching to the GNSS box. The math seems to be relatively an identical between the 2 as each are issues in precision positioning, simply other in scale. In disk drives, it’s nanometers over centimeters and in precision GNSS, it’s centimeters over kilometers.

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How lengthy have you ever been creating the era or way you used to win the competition?

I’ve been creating and keeping up low cost precision GNSS answers within the RTKLIB device for approximately six years however have best labored with smartphone answers within the remaining 12 months or two.

Have you ever participated in earlier editions of this contest?

I didn’t take part in remaining 12 months’s pageant however I did paintings with the information after the competition used to be over and shared an answer the usage of RTKLIB that will have positioned 5th within the pageant.

What accuracies have been you in a position to acquire?

I accomplished a rating of one.648 m at the personal leaderboard. This represents the common of the fiftieth percentile and the ninety fifth percentile of the mistakes as scored by way of Kaggle. Kaggle does now not supply to any extent further breakdown of this quantity however, according to the learning information for which floor truths have been supplied, this corresponded to a fiftieth percentile error of more or less 0.9 m and a ninety fifth percentile error of more or less 2.3 m. With a small tweak to my answer after the contest used to be over, I used to be in a position to support my personal leaderboard rating to one.593 m, which might had been inside of 1 cm of the third-place answer.

What are the important thing options of your way?

My way used to be to make use of the prevailing post-processing kinematic (PPK) answer set of rules in RTKLIB however to reoptimize it for the original traits of the smartphone statement information. A PPK answer is the post-processing identical of a real-time kinematic (RTK) answer and is a differential answer that depends on differencing the receiver observations with observations from a close-by base station to cancel out many of the biggest error resources — together with atmospheric, orbital and clock mistakes — since those mistakes are not unusual between the 2 units of proximate observations.

As a result of smartphones have very deficient GNSS antennas they usually have been fixed within cars, the sign high quality is far decrease and the multipath a lot more than the ones for which the RTKLIB set of rules used to be optimized. As well as, the smartphones have been the usage of the L5 frequency band, while RTKLIB used to be optimized for the extra recurrently used L2 frequency band. One of the crucial major targets of my optimization task used to be to incorporate many low-quality observations within the answer that will most often be discarded, however to de-weight them as it should be.

What end-user packages are you anticipating your way to allow?

RTKLIB device is recently used to offer precision answers for lots of end-user packages comparable to surveying, drone photogrammetry, sports activities monitoring, precision agriculture, application location, marine navigation and floor subsistence tracking. Despite the fact that smartphones received’t substitute devoted low cost GNSS receivers, the difficult nature of the smartphone information critically stresses the RTKLIB algorithms and exposes a large number of alternatives for growth which might be a lot much less evident with extra conventional, upper high quality information. I’ve pulled those enhancements into the principle department of the demo5 model of RTKLIB, and therefore this paintings will have to right away support the standard of some of these packages and prolong their use into more difficult environments.

Photo: Google

Photograph: Google

Acknowledgements: Due to the Institute of Navigation (ION) for co-sponsoring the 2022 Smartphone Decimeter Problem. Due to Luke Walcher and Tolu Ojelade for his or her contributions to the pictures used on this article.