ROVR Docs
Last updated
Last updated
There is no doubt that Advanced Driver Assistance Systems (ADAS) and Autonomous Driving will be the most significant transportation technology revolution in the next 20 years; at the same time, in the consumer electronics field, VR/AR technology will profoundly impact human history, much like smartphones did over the past decade. Behind all this lies the rapid advancement of AI technology. However, beneath the surface lies a massive and concerning limitation: do we have the capability to obtain massive, real-world, and precise 3-dimensional spatial data and 4-dimensional spatial-temporal data? This is not as easy as acquiring text, images, and video data on today’s internet.
For the most pressing application at the moment, Advanced Driver Assistance Systems (ADAS), it heavily relies on high-definition 3D maps, which is an obvious conclusion. However, the shortage and high cost of HD maps have severely constrained the development of related fields. People have had to resort to temporary measures, sometimes deliberately downplaying the importance of HD maps (even out of good intentions and to avoid controversy), such as using non-high-precision, non-real-time traditional electronic navigation maps, combined with multi-sensor fusion, attempting to compensate for the shortcomings in accuracy and operational effectiveness. However, this often leads to reliability and safety issues, and even heartbreaking tragedies.
On the other hand, current HD maps data collection is entirely controlled by centralized companies or organizations; it is expensive, complex, and has a very low data refresh rate. Many maps collected ten years ago are still in use. Some great and admirable DePIN projects, attempt to use decentralized methods with smartphones or dashcams to create maps. However, unfortunately, this approach cannot meet the high-precision and data quality requirements demanded by the automotive industry, leading to the project's inability to truly gain customers and achieve large-scale commercial use. This is due to an unavoidable and practical reason: centimeter-level accuracy HD maps exist as the ground truth system and are the most important infrastructure for ADAS and autonomous driving. 3-dimensional maps made solely by recording 2D video streams with smartphones and dashcams cannot prove to be accurate and stable enough.
VR/AR applications represent another exciting direction. Clearly, they involve the high precision and complete reconstruction of 3D scenes. Generally speaking, the development of social and killer applications is crucial for VR/AR. However, an awkward reality is that due to the lack of real 3D data available for AI training, VR/AR applications often fail to accurately reconstruct 3D scenes in coordination among multiple users and seamlessly integrate with other sensors. Therefore, in the foreseeable future, the shortage of high-precision 3D data will greatly limit the development of the VR/AR ecosystem.
Over the past 20 years, we have gradually come to realize that the traditional method of producing HD maps has reached its limit and cannot support the next generation of intelligent transportation revolution. ROVR will adopt a completely new approach to data collection and map creation, and pioneeringly integrate the traditional automotive industry, consumer electronics industry, Web2, and Web3, to prepare massive data for the upcoming comprehensive AI and 4-dimensional spatio-temporal computing.
1.2.1 High-precision Software and Hardware Systems Conforming to Automotive Industry Standards
ROVR will fully adopt onboard RTK (Real-time Kinematic) and PPK (Post-processed Kinematic) systems, supplemented by AGNSS and integrated navigation, to consistently improve the overall system positioning accuracy to the centimeter level
ROVR will employ automotive-grade mass-produced multi-channel solid-state 3-dimensional LiDAR and onboard survey-grade IMU systems. In the collected raw data, approximately 1 million laser reflection points with an accuracy within 2 cm are generated per second to describe the surrounding scene and for 3D reconstruction.
Traditional image sensors in dashcams and smartphones use rolling shutters, which can cause image distortion when subjects move at relatively high speeds. This distortion significantly impacts measurement accuracy. ROVR will use global shutters and fixed-focus lenses to eliminate the 'jelly effect' and image distortion commonly seen in dashcams and smartphone cameras, ensuring accurate measurements.
By employing enhanced smartphone external devices, complemented by AGNSS, RTK, and other technologies, ROVR will enhance smartphone positioning accuracy to the centimeter level. Additionally, ROVR will capture road dynamic elements in real-time and generate dynamic layers for HD Maps.
1.2.2 Open Ecosystem
ROVR is a highly transparent and autonomous project, where any organization or individual can produce, sell, or even DIY their hardware devices within the open-source framework of the ROVR project. They can also participate in data collection, storage, maintenance, and processing, and can join or leave the ROVR network at any time.
The ROVR community plays a crucial role throughout the entire ROVR network. It consists of members from various roles such as data contributors, data consumers, developers, DIY enthusiasts, hardware manufacturers, and computing platform providers. Together, they ensure the healthy operation of the ROVR network.
Despite being a decentralized open system, the ROVR network still requires an organized entity to manage and operate it. This entity is the ROVR Foundation, which is responsible for coordinating and managing software and hardware development, data collection, quality control, network maintenance, and submitting R2FCs (ROVR Request for Comments) to the community for voting.
1.2.3 Fair Token Rewards
Anyone contributing to the ROVR network, whether through data collection or other means, will receive very fair token rewards. These tokens are distributed based on actual workload and cost input. In other words, it's a return to proof-of-work (POW), but with a more meaningful and environmentally friendly approach, capable of generating significant social and economic value.
1.2.4 Permissionless and Censorship-resistant
Every human driver with a moving vehicle exceeding 15 km/h, every individual with a high-performance server, and even those with just a passion, can become contributors in the ROVR network without any permission required. Contributors remain completely anonymous and are not required to provide any personal information. They have 100% autonomy to decide when to join or leave the ROVR network.
1.2.5 Privacy Protection
ROVR places great emphasis on privacy. Data is structured and anonymized in most cases. For instance, ROVR data collection terminals typically only upload structured data without detailed features of the targets. For example, it might define a "pedestrian" and tag their speed and direction without describing any additional information.
Participating in the ROVR network is straightforward for ordinary users and offers multiple avenues:
1.3.1 Using a "Smartphone + Tarantula Mini" for Data Collection
"TarantulaX Mini" is a highly integrated and compact sensor system, including an enhanced RTK (Real-Time Kinematic) positioning unit, an Inertial Measurement Unit (IMU), and a Web3 identity and data validation module. It connects to smartphones via Bluetooth. It utilizes the smartphone's camera to capture road images, process and classify the data, and upload it, thereby earning $ROVR tokens.
Please note that, in most cases, mainstream smartphones can accomplish this task. ROVR will strive to adapt to various smartphone models as much as possible. However, not all smartphones are suitable for this task, as it depends on hardware and software configurations, particularly the camera's optical parameters, AI computing power, and encryption capabilities.
1.3.2 Using “LightCone” (Professional 3D Lidar Collection Device) for Data Collection
The “LightCone” is a professional device equipped with a multi-channel lidar (>120 channels, 200 meters measurement range), capable of directly sensing the surrounding 3-dimensional environment in real-time. It also integrates automotive-grade integrated navigation systems and large-aperture (>130mm) measurement antennas. The device integrates 4G/5G networks to automatically collect, process, and upload data, earning $ROVR tokens, as well.
During usage, users still need to connect the device to their smartphones via Bluetooth or Wi-Fi. However, the smartphone is only used as a control terminal, with no special requirements and no need to stay online constantly.
It's worth noting that whether it's the “Tarantula Mini” or the “LightCone”, the user device's speed must be between 15 km/h and 140 km/h. Rewards will not be generated if the speed exceeds or falls below this range. The reward is related to the number of kilometers of quality data.
The qualification of data depends on whether it includes paved roads with clear lane markings and other traffic elements. According to road network data definitions, these are classified as Level 8 and above roads, typically encompassing highways, urban expressways, ramps, and city streets.
1.3.3 Providing R-Node nodes and staking $ROVR tokens
R-Node serves as the data storage and processing node for the ROVR network. Users can provide nodes that meet the requirements of hardware, software, and network conditions, and stake $ROVR tokens to earn $ROVR token rewards, including basic rewards and additional workload rewards.
The R-Node node plan will be launched in the second phase of the project. In the initial stage of the project, ROVR will utilize traditional cloud servers and OSS services.
ROVR Inc. is responsible for cleansing, processing, and ultimately transforming the data within the ROVR network into HD maps and other data products, which are then sold to end customers.
ROVR Inc. can provide customized maps or other data services according to the requirements of OEM/Tier 1 or other clients.
When the time is right, similar to hardware development and production, ROVR will open up third-party data processing. Any organization or individual will be able to perform secondary processing on ROVR data and sell it to end users. This will be an important component of the ROVR ecosystem.
There are two project entities: ROVR Foundation and ROVR Inc.
Responsibilities of ROVR Foundation:
Maintain the overall token economics and project ecosystem.
Output hardware reference designs and software designs.
Data collection and storage.
Maintain technological evolution and guiding principles.
Responsibilities of ROVR Inc:
Creating maps products or other forms of data products and selling them to customers.This job will be done by third parties in the near future.
ROVR deeply understands the importance of data privacy and compliance. It's important to note that in most countries and regions, the collection and use of public road map data are generally legal. However, in some countries and regions, the collection of public road map data may be restricted by law. Major considerations include privacy protection, national security, and commercial competition. In a few countries and regions (such as mainland China, etc.), unauthorized surveying is a serious offense. Therefore, in these specific countries and regions, ROVR hardware devices cannot be used, data generated will not be accepted by the ROVR network, and no rewards will be generated.
Given ROVR's decentralized nature, users are advised to understand the relevant laws and regulations of their country in advance to avoid breaking the law or causing unnecessary trouble.
Tarantula Mini serves primarily as an RTK receiver and also signs location data to prevent malicious replay attacks or forgery, ensuring its authenticity and validity.
Here's a brief schematic(Future modifications and upgrades may occur, but updates to this document may not be timely):
Tarantula Mini connects to the NTRIP service provided by RTK providers (e.g. GEODNET) through a smartphone to obtain nearby RTK base station information data. It then acquires real-time observation files from the base stations and satellite ephemeris data to perform real-time position correction at one-second intervals. Typically, Tarantula Mini can improve the positioning accuracy of a smartphone to within 1 cm.
On the other hand, Tarantula Mini uses an unreadable hardware private key to digitally sign high-precision location data to prevent data from being maliciously tampered with or forged.
For detailed technical specifications, please refer to the ROVR open-source repository.
LightCone is a much more complex system compared to Tarantula Mini. It consists of the following components:
Specs: (Future modifications and upgrades may occur, but updates to this document may not be timely)
Detect Range: 200m (905nm Lidar), 500m (1550nm Lidar), @10% NIST
Dimension: 135mm x 220mm x 120mm
Weight: 1.6kg (905nm), 3.3kg(1550nm), not include cables and support parts
Power Consumption: 35w (905nm), 50w (1550nm)
Storage: Onboard 64GB eMMC + 1T SSD
Waterproof: IP67
Accuracy: < 2cm (1sigma)
Camera Resolution: 4 K
Lidar Resolution: 0.1(horizon), 0.2(vertical)
IMU bias instability (Allan):
Gyro: XY < 2.6 degree / h, Z < 2.1 degree / h;
Accelerator: < 45 ug
IMU bias (XYZ): 0.13 degree / √h, 0.050m / √h
AI computing Power: 8 TOPS
Working Temperature: -45 °C ~ 85 °C
Storage Temperature: -65 °C ~ 105 °C
HD maps refer to map data with high spatial and temporal resolutions, typically used in applications such as autonomous driving systems and other scenarios requiring precise positioning. These maps not only contain basic geographical information like road and building locations but also include more detailed information such as lane markings, traffic signals, traffic signs, road curvature, elevation changes, and so on.
In the production process, HD map mapping typically involves the use of high-accuracy sensors and measurement devices such as LiDAR, cameras, Global Positioning System (GPS), etc., to gather map data. This data undergoes meticulous processing and map-making algorithms to generate maps with high accuracy and reliability.
In terms of data content, HD maps typically consist of three main components:
3.1.1 Road Model
This includes information at the road level, such as road geometry, road attributes (such as passability), lane information within the road, and information about objects on the road.
3.1.2 Lane Model
This contains information at the lane level, such as lane geometry, lane connectivity, lane attributes (such as direction of travel, whether it can be crossed), and the relationship with the object model.
3.1.3 Object Model
This encompasses the geometry, orientation, category, and corresponding relationships between objects and lanes.
For HD mapping, we use professional equipment, including solid-state LiDAR, a 4K camera, and high-precision positioning devices.
Parameter calibration is indeed the first step. The internal parameters of the sensors are calibrated during the manufacturing process. As for the external parameters, we have integrated an automatic calibration program, so users do not need to perform this operation.
After users complete data collection, we aggregate the local mapping results in the cloud. Typically, the requirements for HD maps are an absolute accuracy of less than 50 cm and a relative accuracy of less than 20 cm within 100 meters.
All of this data is used to automatically generate HD map data. Point cloud data provides centimeter-level ranging capabilities, while image data offers rich semantic information. Precise pose data improves the overall consistency and positional accuracy of HD maps. However, due to the high precision requirements set by OEMs, manual editing and review are still necessary after automatic generation.
Starting in Q3 2024, we will use the mini device TX for HD map mapping, and our app will support online mapping. Currently, the data transmission rate is 100kB/km, and it is expected to decrease to 50kB/km by Q2 2025.
After Q2 2025, we will release our professional device LC, which will directly support online mapping
For HD map updating:
Firstly, we will perform 3D reconstruction of consecutive images using the stereo vision method.
However, due to the latency during camera exposure, the result of visual 3D reconstruction is non-rigidly related to the existing HD map point cloud. We will simulate the imaging process to project the HD map point cloud onto the image coordinate system and match it with real-time collected images (mainly edge features) to optimize the camera's intrinsic and extrinsic parameters.
We optimize the camera's intrinsic and extrinsic parameters because the smartphone holder cannot maintain a rigid connection for a long time, causing the smartphone's extrinsic parameters to change. Additionally, the presence of the windshield can cause changes in the relative position between the smartphone lens and the windshield, leading to changes in the entire measurement system's intrinsic parameters.
After obtaining the optimized camera parameters, we will estimate the depth for each pixel, estimate the accurate depth information through monocular depth estimation, and compare it with the existing HD Map to complete the HD Map updating.
In the era of LLM, data is a precious resource, and its importance is particularly prominent in the field of vision. Unlike SORA, our 4D data generation model is based on the real world and allows for precise scene editing (such as defining accurate three-dimensional positions, dimensions, orientations, velocities, etc., for a vehicle), which is crucial for training large models for tasks like autonomous driving. Below is a demo video based on the Kitti dataset. In the video, we replaced all different types of vehicles with the same vehicle model.
Furthermore, we have obtained LOI from a Tier 2. However, for commercial reasons, the video we are showcasing is generated using the KITTI dataset, a opensource autonomies driving dataset.
“Thank you very much for your strong support for the binocular data generation project based on AIGC technology. After evaluation by the R&D team, the generated data and annotation results provided by your company meet the training requirements of the binocular perception model. I am sending this email to confirm our cooperation. Please start the formal development work from now on. The specific amount and deliverables shall be subject to the commercial contract”
— LOI email sent to us by a Tier 2 company
The data we generated has completed usability validation, and the client is already using it in a real production environment.
Cheating and spoofing is a common issue in DePIN projects. ROVR has learned from the lessons of many DePIN projects. With multiple anti-cheating / anti-spoofing technologies, ROVR will no longer require a centralized denylist to ban malicious users. In the ROVR network, all nodes are designed as trustless models, and the system only accepts verified data—nothing more. Users can attempt to cheat without worrying about being denylisted. However, cheating and spoofing will simply not succeed.
All ROVR hardware is designed or integrated with a specialized encryption chip. This chip has a feature where it stores a private key that cannot be read from the outside but can be used to digitally sign the output data of ROVR hardware devices.
During output, ROVR hardware devices, including TarantulaX and LightCone, use the encryption chip to sign their data. When sending data, the device transmits the data itself, the signed digital fingerprint, and the public key of the encryption chip. The data is also packaged according to the blockchain's data structure. The ROVR backend system receives the data and uses the public key to verify the signature, ensuring the authenticity of the data. Data with failed digital signatures will be discarded.
In DePIN projects, spoofing of device latitude and longitude information is common, with a common approach being NMEA message spoofing to carry out replay attacks. However, in addition to providing GPS latitude and longitude data, ROVR hardware devices also include raw satellite observation files and precise ephemeris files.
These files include information such as GPS time, quantity of satellites observed at that moment, their positions, velocities, heading, and DOP, etc. Additionally, this data is compared with parameter information from nearby base stations provided by RTK service providers (currently GEODNET). This makes spoofing highly complex. Additionally, for the quad-constellation GNSS system used by ROVR, the precise ephemeris files come from the GPS、GLONASS、Galileo and BeiDou. Forging such files without detection would likely be a feat only achievable by extraterrestrials.
Easily spoofed NMEA messages
Imagine forging this 250,000-line file:
ROVR hardware includes multiple sensors that create synergistic effects in the same scene. For example, when a user drives over a speed bump, the image will obviously shake. At the same time, the IMU will detect this shake, the LiDAR will experience the same shake, and the RTK system will also reflect similar vibrations in the precise positioning data.
A malicious user attempting to forge values from four different sensors and simulate the same kinematic model would find it extremely challenging.
ROVR Reward Points are designed for contributors to the ROVR project. Before the official token issuance ($ROVR), data collection contributors will be recorded and receive points as rewards. During the TGE (Token Generation Event), these reward points will be converted into $ROVR tokens at a certain ratio (ideally 1:1 or a similar rate, depending on user growth and external/internal factors at the time of TGE) and distributed as airdrops.
$ROVR is the token of ROVR Network in the future. Contributors can earn $ROVR rewards by using ROVR devices (TarantulaX, LightCone) to collect data, or provide data storage nodes, or by staking on storage nodes.
ROVR's Tokenomics is designed to incentivize contributors, maintain the balance of the ROVR ecosystem, and establish pricing and token value for future data consumers. When $ROVR is issued in the future, it will strictly adhere to the principles of transparency and fairness.
Total fixed supply: 10 billion
Initial minting amount: 10 billion
Issuance Network: Solana (Peaq in the future)
Distribution structure (subject to adjustment):
50% as rewards for contributors, to participate in building the ROVR Network
20% for project founding members, previous equity investors, and future global core contributors for project R&D and system construction
14% for investors and future public sale to the project issuance
10% for market operations, future liquidity supplements, and reserved for Advisors (if any)
4% for the ROVR Foundation to maintain subsequent ongoing management and supervision
2% as Airdrops to reward early activity and test participants
Category
% of Supply
Token Amount
Community Contributors
50.00%
5,000,000,000
Teams(Previous Equity Investors, Founders, Core Contributors)
20.00%
2,000,000,000
Investors / Public sale
14.00%
1,400,000,000
Marketing Operations / Liquidity / Advisors
10.00%
1,000,000,000
Foundation / Ecosystem
4.00%
400,000,000
Airdrops
2.00%
200,000,000
5.4.1 Emission Overview
Fixed Supply: 5 billion
Initial Minting: 5 billion
Time Scale: $ROVR will be emitted over 521 weeks (10 years) to contributors until the cap of 5 billion is reached
5.4.2 Emission Schedule
Decreasing release model: Emissions follow a decreasing weekly schedule below
Initial release rate: The token will be emitted in first week of 15.00% annualized (or 0.29% weekly)
Decreasing rate: Each subsequent week will follow a fixed decreasing rate of ~8.78% annualized (or 0.17% per week).
Example Emissions Calculation:
First Week Emissions1 = 5,000,000,000.00 * Expansion Release Rate1 The initial Expansion Release Rate (Expansion Release Rate1) = 0.288461538461538% (15.00%/52) Emissions1 = 5,000,000,000.00 * 0.02191780821917810000% = 14,423,076.9230769000
Second Week Emissions2 = 5,000,000,000.00 * Expansion Release Rate2 Expansion Release Rate2 = Expansion Release Rate1 * (1 - Decreasing Rate) Disinflation Rate (fixed) = 0.168894408433938% Expansion Release Rate2 = 0.288461538461538% * (1 - 0.168894408433938%) = 0.287974343052594% Emissions2 = 5,000,000,000.00 * 0.287974343052594% = 14,398,717.1526297000
Third Week Emissions3 = 5,000,000,000.00 * Expansion Release Rate3 Expansion Release Rate3 = Expansion Release Rate2 * (1 - Decreasing Rate) Decreasing Rate (fixed) = 0.168894408433938% Expansion Release Rate3 = 0.02191473528801470000% * (1 - 0.168894408433938%) = 0.287487970489454% Emissions3 = 5,000,000,000.00 * 0.287487970489454% = 14,374,398.5244727000
5.5.1 Base Reward
For TarantulaX device users, a base reward of 30 $ROVR is given for every 100 kilometers covered.
For LightCone device users, a base reward of 300 $ROVR is given for every 100 kilometers covered.
Area Exploration Effect: Each road (separated by direction) earns a full reward the first time it's covered within a natural week.
Reward Decrease: For each road, the rewards are calculated based on the order of visits, regardless of the user.
1st revisit: 100% of the base reward
2nd revisit: 80% of the base reward
3rd revisit: 64% of the base reward
And so on, with each revisit reducing the reward by 20%.
Reset Time: Rewards reset every Monday at 00:00 GMT.
🎉 Please Note: To expedite initial mapping, before July 1st, 2025, at 00:00 GMT, the area exploration rewards will not decrease. The reward reduction policy will take effect starting from July 1st, 2025, at 00:00 GMT or when 80% of the total weekly release has been reached within a week.
The collection of contributors in terms of data quality will also impact the rewards. The specific reward depends on the accuracy, completeness, and clarity of the data, as detailed in Score Dimensions.
For TarantulaX:
Maximum Reward Multiplier Users who achieve the highest scores in the scoring dimensions 1-5 will have their token rewards multiplied by 1.2x. For the sixth scoring dimension, RTK Fixed Solution, users will receive a 2x reward multiplier
Maximum Reward Multiplier With 6 scoring dimensions, the maximum multiplier will be: 1.2 * 1.2 * 1.2 * 1.2 * 1.2 * 2≈5x (4.98x), which means that contributors who collect high-quality data will receive approximately: Base Reward 30 $ROVR * 5 = 150 $ROVR per 100 kilometers
For LightCone:
Maximum Reward Multiplier: In addition to the scoring dimensions of TarantulaX, two additional evaluation dimensions—Outlier Ratio and Point Cloud Density—are included. The reward coefficients for the first 6 scoring dimensions are halved, while the reward coefficients for Outlier Ratio and Point Cloud Density are both 1.5.
Maximum Reward Multiplier: With 8 scoring dimensions, the highest multiplier will be: (1.2 * 1.2 * 1.2 * 1.2 * 1.2 * 2) * 0.5 * 1.5 * 1.5 ≈ 5.60x. This means contributors who collect high-quality data will receive: Base Reward 300 $ROVR * 5.60 = 1680 $ROVR per 100 kilometers.
5.5.3 Reward Distribution
If the total number of tokens earned by all users in a week exceeds the total token release for that week, the rewards will be distributed proportionally based on individual driven road mileage
The settlement cycle is 1 week: data collection reward generated in the current week will be paid by 16:00 on Tuesday of the following week
Sharpness
Evaluation Method: Use image processing algorithms to calculate the sharpness of the image.
Indicator: Laplacian Energy
Reward Settings:
No Reward: Laplacian Energy < 40
Basic Reward: 40 ≤ Laplacian Energy < 60
Highest Reward: Laplacian Energy ≥ 60
Brightness
Evaluation Method: Calculate the average brightness value of the image.
Indicator: Average grayscale value (0-255)
Reward Settings:
No Reward: Average grayscale value < 80 or > 200
Basic Reward: 80 ≤ Average grayscale value < 100 or 180 < Average grayscale value ≤ 200
Highest Reward: Average grayscale value 100 ≤ Average grayscale value ≤ 180
Contrast
Evaluation Method: Calculate the standard deviation of pixel grayscale values in the image.
Indicator: Standard Deviation
Reward Settings:
No Reward: Standard Deviation < 30
Basic Reward: 30 ≤ Standard Deviation < 50
Highest Reward: Standard Deviation ≥ 50
Dynamic Range
Evaluation Method: Calculate the ratio of the maximum brightness value to the minimum brightness value of the image.
Indicator: Maximum brightness / Minimum brightness
Reward Settings:
No Reward: Maximum brightness / Minimum brightness ratio < 4
Basic Reward: 4 ≤ Ratio < 7
Highest Reward: Maximum brightness / Minimum brightness ratio ≥ 7
Noise Level
Evaluation Method: Use image processing algorithms to detect noise in the image, which can be measured by calculating the signal-to-noise ratio (SNR) of the image or the residuals after using a denoising algorithm.
Indicator: Signal-to-Noise Ratio (SNR)
Reward Settings:
No Reward: SNR < 20 dB
Basic Reward: 20 ≤ SNR < 25 dB
Highest Reward: SNR ≥ 25 dB
Accuracy of RTK:
RTK is Fixed Solution
Reward Settings:
Basic Reward: RTK not Fixed Solution ratio < 60%
Highest Reward: RTK Fixed Solution ratio ≥ 60%
Outlier Ratio:
Evaluation Method: Outlier ratio is used to detect points in the point cloud that are far away from the other points, which are usually considered noise or errors in measurements. It is calculated by comparing each point's distance to its nearest neighbors and identifying points that are significantly farther than the others.
Indicator: Outlier Ratio, Proportion of points in the point cloud that are considered outliers.
Reward Settings:
No Reward: Outlier ratio > 10%
Basic Reward: 0.05 ≤ Outlier ratio ≤ 10%
Highest Reward: Outlier ratio < 5%
Point Cloud Density:
Evaluation Method: Point cloud density is evaluated by calculating the number of points in a given area. A higher density means more detailed and accurate data. The density is computed by dividing the number of points by the area they cover.
Indicator: Point Cloud Density, Number of points per unit area (e.g., points per square meter).
Reward Settings:
No Reward: Point cloud density < 10 point/m^3
Basic Reward: 10 ≤ Point cloud density < 20 points/m^3
Highest Reward: Point cloud density ≥ 20 points/m^3
Copy
5.6.1 Burn Mechanism Overview
The burn mechanism is aimed at data consumers or data customizers who will pay for data services using fiat currency or stablecoins. In this process, the ROVR Foundation will use the acquired fiat currency or stablecoins to buy back $ROVR from the market, and then burn 100% of it. The process is as follows:
5.6.2 Data Pricing
According to the current mapping costs of Map Suppliers in the market, each kilometer per year of HD map costs approximately $1,800 (referring to highways)
According to the Navigation Data Standard (NDS) level 13 grid and corresponding to planar projection, we can divide the area into Geo Units. A GU is generally a square of 22.5km * 22.5km, as shown in the following figure:
According to our statistics, a certain country has 1,536,000 GUs, with approximately 300,000 kilometers of highways, and the pricing for HD maps by Map Suppliers within the country is basically consistent with each kilometer $1,800 per year
Based on the above data, we can infer that under Navigation Data Standard (NDS) level 13, the market pricing for each Geo Unit (6.25 square kilometers) is approximately $6.76 per week, calculated as follows: $1,800 * 300,000 kilometers / 1,536,000 Geo Units / 52 weeks = $6.76
Considering this pricing is only for highways, the actual pricing for city roads would be higher. ROVR project building on this foundation is committed to addressing the issues of high collection costs and untimely updates of HD maps. Our initial pricing is set at $3.5 per Geo Unit per week. This represents a reduction of about 50% compared to traditional data collection prices!
5.6.3 Token Burn
For future data consumers purchasing ROVR data services, the ROVR Foundation will use the received fiat currency or stablecoins to buy back $ROVR on the secondary market, and then burn 100% of it. During this process, the ROVR Foundation will not engage in any profit-making activities and will promptly announce the number of tokens burned.
5.6.4 Incentive Reward
For ROVR contributor, if a region is selected by a data consumer, then to incentivize this region data collection, we will also introduce a reward multiplier for that region, which is similar to the the RTK Fixed Solution model will start at a minimum of 2x, and will be adjusted increase by ROVR based on specific circumstances in the future
