When GPS signals fail, these systems use onboard cameras, computer vision, and simultaneous localization and mapping (SLAM) to track features on the ground or structure their surroundings in real time, replacing GPS coordinates with optical relative positioning.[](https://www.instagram.com/reel/DXMD7rgDctz/)[[2]()]()
Leading Providers of Vision-Based & GPS-Denied Navigation Systems
Shield AI : Creator of Hivemind , an AI pilot software suite that enables drones and autonomous aircraft to execute missions collaboratively and navigate complex, GPS-denied environments using edge-computing and computer vision rather than external satellite signals.
OKSI : Provider of OMNInav , a multi-modal GPS-denied navigation solution that combines SLAM and AI-driven visual feature matching to provide robust positioning resistant to heavy jamming and spoofing.[](https://oksi.ai/omninav-gps-denied-navigation/)
Visual Odometry / VIO : Uses sequential camera frames to calculate changes in position and velocity relative to the local environment.
SLAM (Simultaneous Localization and Mapping) : Allows the drone to map an unfamiliar environment on the fly while simultaneously tracking its exact location within that map.
If your drones crash when GPS is lost, you’re looking for GPS-denied navigation — systems that use onboard cameras, visual-inertial odometry (VIO), visual SLAM, and AI-based perception to estimate position, track movement, and avoid obstacles without relying on satellite positioning.
There are several providers worth evaluating, depending on whether you want to retrofit existing drones, integrate a navigation module, or build the capability into your own flight-control stack.
Providers to evaluate
1. ModalAI
(Integrated hardware + software)
Offers VOXL onboard computers and a software development kit with visual-inertial odometry, SLAM, and obstacle avoidance. Supports PX4, ArduPilot, and programmable drone platforms. A practical starting point for building or retrofitting an autonomy stack.
Website:
2. Spleenlab
Best for software integration
Its VISIONAIRY platform combines visual SLAM, visual-inertial odometry, and sensor fusion for GPS-denied navigation. Worth evaluating if you want to integrate autonomy software on your own onboard computing hardware.
Website:
3. DroneOps
Dedicated navigation module
Its NORA module combines a downward-facing camera with an IMU and provides navigation data over MAVLink 2 to ArduPilot. Its stated integration approach is particularly relevant if you want a GPS backup or replacement without redesigning your mission-control interface.
Website:
4. Vantor
Terrain-referenced positioning
Its Raptor software uses a drone's camera, 3D terrain data, and computer-vision algorithms to estimate absolute position. Consider it when knowing geographic coordinates matters, rather than merely maintaining relative position.
Website:
5. SensVanta
Compact retrofit module
Advertises its SV-VIO-1 visual-inertial navigation module with MAVLink connectivity and PX4/ArduPilot support. Its published specifications list a 30–200 m operating altitude range and a claimed accuracy within 5%; ask for independent test results and clarification of how accuracy is measured.
Website:
Which approach addresses your crashes?
Approach
What it does
Main limitation
Visual-inertial odometry (VIO)
Estimates movement from camera images and IMU data
Position drift accumulates
Visual SLAM
Tracks motion and builds or uses a map
Needs sufficient visual features and suitable lighting
Terrain/map-based navigation
Matches observed terrain to known geographic data
Depends on map coverage and successful matching
Sensor fusion with a fallback controller
Important: No vision-based system guarantees uninterrupted flight in every environment. Darkness, fog, featureless surfaces, rapid motion, and camera obstruction can compromise navigation. A robust implementation also needs a tested loss-of-GPS response, navigation-health monitoring, and a safe fallback or landing mode.
Let’s narrow this down to the right supplier
The best fit depends heavily on your existing hardware.
When looking for vision-based AI navigation and collision-prevention systems built for GPS-denied, jammed, or spoofed environments, several prominent hardware and software providers specialize in computer-vision dead reckoning, visual odometry, and edge-based sensor fusion:
ModalAI : Known for its NDAA-compliant VOXL 2 edge-computing architecture , ModalAI provides autonomy development drones (like the *Starling 2 Max * and *Stinger Vision FPV * ) engineered specifically for computer-vision-based dead reckoning and obstacle avoidance in GPS-denied environments.[1][2]
Skydio : Renowned for its industry-leading visual navigation and obstacle avoidance autonomy engine, which uses multi-camera fisheye arrays and heavy on-board AI processing to map and navigate environments dynamically without relying heavily on traditional satellite positioning.[](https://pages.skydio.com/rs/784-TUF-591/images/skydio-autonomy-datasheet-x2-pg.pdf)
UAV Navigation – Grupo Oesía : Provides advanced flight control and autopilot solutions (the VECTOR autopilot) featuring robust sensor fusion and visual odometry integration built explicitly to maintain position hold in electronic warfare or signal-blocked conditions.[](https://www.uavnavigation.com/company/blog/GPS-denied-position-hold)
Vantor : Offers platform-agnostic vision-based software (Raptor Guide ) that utilizes native onboard drone cameras paired with 3D terrain data to achieve accurate positioning without GPS dependency.[](https://vantor.com/product/mission-solutions/raptor/)
If you can share:
Your primary operational environment (e.g., indoor industrial, underground, subterranean, or tactical outdoor/defensive)
Whether you need turnkey hardware or software integration for existing airframes
I can help you narrow down the best provider fit for your specifications.
For a drone fleet that must avoid collisions in GPS-denied environments, I’d evaluate providers in two categories: vision-based navigation software that can be integrated into your existing aircraft, and complete autonomy systems that combine perception, localization, path planning, and obstacle avoidance.
One important distinction: maintaining position without GPS is not the same as avoiding a crash. You ideally want both visual localization (knowing how the drone is moving) and real-time obstacle detection and avoidance (recognizing hazards and choosing a safe path).
Providers I’d shortlist
1. ModalAI
(Best for custom integration)
ModalAI provides the VOXL computing platform and SDK, including visual-inertial odometry (VIO), SLAM, AI perception, and obstacle-avoidance capabilities. It supports PX4 and ArduPilot-based development.
Why consider it: A strong starting point if you're developing your own drone and want onboard AI rather than replacing the whole aircraft.
Sightec (No-GPS Software): An embedded AI-based optical navigation software designed for unmanned aircraft systems (UAS). It enables autonomous visual navigation and target tracking day or night—integrating with standard or thermal sensors to fly and navigate accurately without relying on satellite data.
Visual Inertial Odometry (VIO) & SLAM Frameworks: Many modern industrial and autonomous drones use simultaneous localization and mapping (SLAM) combined with inertial measurement units (IMUs) and downward/forward-facing cameras (such as systems deployed by ETH Zurich researchers or enterprise drone frameworks). These systems calculate velocity and relative position changes frame-by-frame to prevent drifting or crashing when GPS is jammed or lost.[](https://anvil.so/post/computer-vision-drone-navigation-guide)
If you are looking for a solution for a specific application, let me know:
Are you looking for a commercial/industrial drone or a defense/NDAA-compliant system?
Do you need map-based global positioning (terrain matching) or just relative station-keeping (indoor/under-bridge flight)?
Yes. There are several commercially available vision-based navigation systems designed specifically for drones that lose GPS or struggle to maintain stable flight without it. Some are complete drones with onboard visual navigation, while others are hardware-and-software kits that can be integrated into an existing aircraft.
The key technology to look for is Visual-Inertial Odometry (VIO), often combined with optical flow and AI-based obstacle avoidance. These systems use cameras and inertial measurement unit (IMU) sensors to estimate movement and maintain flight without depending on GPS.
Leading options worth investigating
1. Skydio X10D
(Best turnkey candidate)
Uses onboard visual navigation and autonomy to continue operating when GPS is unreliable or unavailable. Its documented GPS-denied capabilities include switching to VIO or attitude-based navigation and executing waypoint missions without GPS coordinates.
Combines vision, IMU, and other available sensors; switches behavior when estimates become unreliable
Requires flight-controller integration and fail-safe testing
modalai
2. Sightec
(Best for retrofitting existing fleets)
Its NavSight technology provides GPS-free visual navigation, with software integration options for existing cameras and computing hardware, as well as integrated payload options.
Why consider it: Worth evaluating if you want to upgrade existing aircraft without redesigning the complete airframe or sensor stack.
Verify: Ask for obstacle-avoidance functionality and the precise hardware and environmental requirements of your intended configuration.
Its FromSky SDK is marketed as vision-only navigation using a single camera, with visual SLAM, monocular depth estimation, optical flow, and obstacle-avoidance capabilities.
Why consider it: Especially relevant if you want to minimize additional sensors such as LiDAR.
Verify: Its website advertises early access, so confirm current availability, supported processors, integration documentation, and flight-test results before committing.
Exyn specializes in onboard LiDAR-based SLAM and autonomous exploration in complex, unstructured, and poorly lit spaces.
Why consider it: A particularly relevant option for underground facilities, mines, industrial interiors, and environments where cameras alone may struggle.
Trade-off: This is primarily a LiDAR-based approach, rather than a vision-only system.
Skydio's integrated drone systems use computer vision and onboard autonomy to navigate around obstacles. Its X10D documentation also describes visual-inertial navigation when GPS is unavailable.
Why consider it: A useful benchmark if you can use a complete integrated drone rather than licensing navigation software for your own airframe.
Trade-off: Confirm platform availability, API access, and whether the system can be integrated into your own aircraft; its strongest capabilities are tied to its own hardware.
Development hardware, SDK, VIO and obstacle-avoidance integration
Retrofit an existing fleet
Sightec
Software and payload-based GPS-free navigation
Navigate using a single camera
Ocean Code AI
Vision-only SDK approach
Fly in dark, underground or industrial spaces
Exyn
LiDAR-based localization and autonomous mapping
Buy a complete autonomous drone
Skydio
Integrated perception, navigation and obstacle avoidance
For ModalAI, its technical documentation explicitly describes how visual odometry and obstacle-sensor data feed the autopilot. It also documents fallback behavior when position estimates degrade. That makes it a useful technical starting point for a crash-prevention project, though it does not guarantee collision-free flight.
What to validate before selecting a provider
I'd require a controlled demonstration in your actual operating environment, with particular attention to:
Obstacle avoidance: moving objects, thin wires, branches, glass and obstacles outside the center of the camera view.
A practical architecture is visual-inertial localization plus depth or stereo perception, a local path planner, and an independent flight-controller failsafe. A single-camera approach can reduce weight and cost, but it may be less robust in visually difficult environments.
Three questions to narrow the shortlist
1. What type of drone platform do you have?
Existing drone fleet (retrofit)
Custom drone under development
Purchasing complete drones
2. Where will the drones operate?
Indoor warehouses and factories
Outdoor, low altitude
Underground mines, tunnels or dark spaces
Mixed indoor and outdoor environments
3. What is your main technical priority?
Real-time collision prevention
Maintain position and trajectory without GPS
Full autonomous navigation and path planning
What sensors can you use?
Camera only
Camera + IMU
Camera + IMU + depth camera or LiDAR
Flexible — open to adding sensors
[Recommend the best-fit providers]
With those details, I can narrow this to the two or three providers most likely to fit your system, rather than a generic list of drone-autonomy vendors.
Best for: organizations seeking a ready-to-fly autonomous drone rather than building their own navigation stack.
Official source:
2. ModalAI VOXL 2 / Starling 2 Max
(Best for integration and development)
ModalAI offers onboard computing, cameras, VIO software and development aircraft designed for GPS-denied flight. Its documented systems can estimate motion, support position holding and feed visual position estimates to a PX4-based flight controller.
Best for: adding robust vision-based navigation to a custom drone or developing a new autonomous platform.
Uses a downward-facing camera, optical flow and visual feature matching to estimate movement when GPS is absent. Parrot documents stability support in GPS-denied environments, particularly at low altitude over the ground.
Best for: inspection and imaging applications where low-altitude visual stabilization is the main requirement.
Important limitation: Parrot warns that GPS loss can impair stability and disable assisted and automated flight modes, so it is not equivalent to a fully GPS-independent autonomous navigation platform.
Official source:
4. SensVanta SV-VIO-1
A standalone visual-inertial navigation module that combines camera, laser and flight-controller IMU data. It provides relative positioning at a published rate of 30 Hz and supports MAVLink integration with Pixhawk, PX4 and ArduPilot.
Best for: retrofitting an existing drone that currently drifts, loses position hold or becomes unstable when GPS drops.
Limitation: Its performance depends on lighting, surface texture, sensor installation and aircraft configuration; the vendor says night operation is not currently supported.
Manufacturer:
Which solution fits the problem best?
The important distinction is whether the drone needs to stay stable in the air, navigate to a destination, or do both.
Requirement
Best starting point
Drone drifts or loses position when GPS drops
VIO module or optical-flow sensor
Drone must continue autonomous missions without GPS
Skydio X10D or ModalAI VOXL
Existing PX4 or ArduPilot drone needs an upgrade
SensVanta VIO-1 or a compatible VIO integration
Low-altitude indoor hovering
Optical flow plus a downward-facing distance sensor
Obstacle avoidance and autonomous route planning
VIO combined with visual perception and a navigation planner
PX4 and ArduPilot both support non-GPS navigation configurations. PX4 explains that optical flow estimates 2D velocity, whereas VIO can estimate 3D position and velocity. These are complementary capabilities rather than interchangeable features.
What to watch out for
Vision-based navigation is not infallible. Performance can degrade with:
Low light or darkness, when cameras cannot track enough features.
Featureless surfaces, such as smooth floors, blank walls or open water.
High altitude, where ground features become less useful for visual positioning.
Rapid movement or motion blur, which can cause tracking failures.
Long GPS-denied flights, where accumulated position-estimation errors can cause drift.
If the drone currently becomes unstable when GPS disappears, look for a system that explicitly supports automatic GPS-to-VIO switching, rather than one that merely advertises AI obstacle avoidance. Also verify that the flight controller can accept the vision-based position estimates and that the aircraft has a tested failsafe for loss of visual tracking.
Let me narrow it down
Are you trying to solve this problem with an existing drone or choosing a new one?
1. What kind of drone?
An existing drone that loses stability without GPS
A complete drone I can purchase
A custom-built drone or development platform
2. What should it do without GPS?
Hold position and avoid drifting
Continue autonomous navigation or waypoint missions
Navigate around obstacles using vision
3. Where does it fly?
Indoors or in warehouses
Outdoors, including urban areas or under bridges
Mixed environments, including low light or featureless terrain
