@Moderator Many thanks for your reply
Our goal is to develop a drone capable of indoor/confined-space autonomous flight without GPS, using SLAM-based navigation. The system should perform 3D mapping, obstacle avoidance, and full-area path planning.
We would like to confirm the correct set of components and the required algorithm stack.
Planned Algorithm Stack:
State Estimation: QVIO (Visual-Inertial Odometry) providing position and velocity to PX4/EKF2 via voxl-vision-hub.
Mapping: voxl-mapper (TSDF/ESDF) using a depth source.
Planning and Avoidance: voxl-mapper’s path planner or PX4 avoidance, using VIO as the pose source.
Autopilot: PX4 running on VOXL2 via voxl-px4.
Proposed Hardware:
VOXL2 Autopilot –
https://www.modalai.com/products/voxl-2?variant=39914779836467
VOXL2 Tracking Camera (Mono Global-Shutter) – required for QVIO (please confirm the correct part number). --
https://www.modalai.com/collections/image-sensors/products/msu-m0149-1
VOXL2 Compatible adapter
https://www.modalai.com/products/mdk-m0076-1
VOXL2 Time-of-Flight (ToF) Depth Sensor –
https://www.modalai.com/products/m0178?variant=48528287793456
VOXL2 Mini 40-Pin Connector ToF Adapter (MDK-M0172-1-00) –
https://www.modalai.com/products/mdk-m0172-1-00
Questions:
Please confirm if this set (VOXL2 + Tracking Camera + Camera adapter + ToF sensor + ToF adapter) is sufficient for robust indoor SLAM navigation using PX4.
For the tracking camera, which exact ModalAI part number is recommended for QVIO on VOXL2?
Are there any best-practice configurations for voxl-mapper when using ToF instead of stereo depth?
Would you recommend any additional sensors for improved performance in larger or more complex indoor environments?
Goal Summary:
We want the drone to hold position, generate a 3D occupancy map, avoid obstacles, and autonomously plan paths throughout the flyable space—without any GPS input.
Thank you in advance for your guidance and confirmation.
Best regards,
Nitin