voxl-mapper local planner: "Cannot insert trajectory. Total segments = 20, Max segments = 20" — replanning never reaches the drone during Follow Path

[김진혁]

We flew our first mapper-based path flight on a Starling 2 Max and the drone did follow the planned path. However, the local planner was unable to deliver a single trajectory for the entire flight, so the drone was flying on the one path computed before departure, with no replanning.

What happened

Global planning succeeded:

05:13:38  Client requested plan to location
05:13:38  Using start pose of: x: 1.67, y: -0.80, z: -1.46
05:13:38  Using goal pose of:  x: 5.16, y: -1.34, z: -0.89
05:13:38  Starting RRTConnect planner
05:13:38  Immediate collision free path found!

Then, from the instant we pressed Follow Path, this repeated continuously for 2 min 16 s until we took manual control:

05:14:10  Client requested to follow last path
05:14:10  Local Planner failed to send trajectory
05:14:10  ERROR: Cannot insert trajectory. Total segments = 20, Max segments = 20
...       (repeats at roughly 1-2 s intervals through 05:16:26)

Throughout those 2 min 16 s the planner itself was running normally — A* took = 0.07-0.14 ms for 13 iterations and Smoother took = 6-12 ms were interleaved with every failure. So it computes a trajectory and then cannot insert it.

The drone moved 2.68 m straight-line / 8.79 m total path at up to 1.04 m/s, which matches the pre-computed path. It stopped 0.81 m short of the goal only because the pilot intervened.

What we ruled out

1. Not a planning failure — the global path was found and reported collision-free.
2. Not a VIO problem — dt held at 33.3 ms for all 44 samples in flight, features 15-30, cs_ev_pos / cs_ev_yaw / cs_ev_hgt all True.
3. Not a map problem — voxl_mapper_aligned_ptcloud was producing 1,489-2,925 points at ~10 Hz before and during the flight.
4. Not CPU load — load average dropped from 7.75 on the ground to 4.5 in flight; governor was performance.
5. Not thermal throttling — 94-96 C, but scaling_cur_freq equalled cpuinfo_max_freq on all cores and dmesg showed no throttle events.
6. Not a config we changed — voxl-mapper.conf is stock; loco_num_segments is 10.

What we could not find

The limit of 20 does not appear in voxl-mapper.conf. loco_num_segments is 10, which is not the number in the error. We could not locate a setting that exposes Max segments.

One thing we suspect, but have not tested

Before the successful Go, we had two failed ones — both rejected with ERROR: End point is in collision because the goal came in at z: 0.00 (we were clicking in the 2D view, which puts the point at ground level). The Max segments = 20 error appeared immediately on the first Follow Path, which makes us wonder whether failed plans leave segments behind. We have not yet tested whether pressing Clear Paths first avoids it.

Questions

1. Where is the 20-segment limit set, and is it configurable?
2. What normally fills the segment buffer, and what clears it? Does a failed Go leave segments allocated?
3. Is Clear Paths the correct way to reset this state before Follow Path?
4. Is there a way to detect this condition other than reading the mapper journal? From the Portal UI everything looked correct — the path was drawn and Immediate collision free path found! was reported — so an operator has no indication that obstacle avoidance is not active.

Environment

• Hardware: Starling 2 Max, SKU MRB-D0012-4-V4-C29-T8-M36
• voxl-version: system-image 1.8.08-M0054-14.1a-perf, voxl-suite 1.6.4~beta5
• voxl-mapper 0.2.4, voxl-vision-hub with offboard_mode: "trajectory"
• Services active at the time: voxl-vision-hub, voxl-open-vins-server, voxl-mapper, voxl-px4, voxl-portal

We have the full mapper journal and the PX4 .ulg from this flight and can share either.

[Anthony Kang]

안영하세요 진혁씨,

Apologies for the late response. We are currently configuring a Starling Max 2 to replicate your setup and will get back to you with more information by the end of this week. Thanks!

[juancho248]

Title: Starling 2 Max – GPS-Denied Autonomous Navigation Inside Rose Greenhouses

Hello ModalAI Engineering Team,

We are developing FloraVision AI, an agricultural computer vision platform for commercial rose production in Ecuador.

We are evaluating the Starling 2 Max for autonomous indoor flights inside commercial rose greenhouses.

Operating environment:

• Location: Tabacundo, Ecuador.
• Altitude: approximately 2,800–3,000 meters above sea level.
• GNSS-denied greenhouse environment.
• Metal structures, support wires, plastic roofing, and dense vegetation.
• Visually repetitive rows of rose plants.
• Requirement for repeatable image-acquisition missions above crops.

We would appreciate technical guidance on the following questions:

1. Which onboard localization and navigation capabilities are available for autonomous flight without GNSS?
2. How reliable is visual-inertial localization in repetitive greenhouse rows with limited visual features?
3. Can the platform execute predefined and repeatable trajectories without GPS?
4. What software components, SDKs, or additional development are required to implement those trajectories?
5. Which onboard sensors are used for obstacle detection and avoidance, and what limitations should we consider around thin wires, foliage, and greenhouse structures?
6. Does ModalAI have documentation or examples for autonomous indoor waypoint navigation using Starling 2 Max?
7. What safety measures and fallback behaviors are recommended if localization becomes unreliable?

We understand that application-specific development is our responsibility. We are seeking confirmation of the platform's existing capabilities, technical limitations, and recommended development resources.

Thank you for your guidance.

FloraVision AI Engineering Team
Ecuador

[juancho248]

Title: Starling 2 Max – GPS-Denied Autonomous Navigation Inside Rose Greenhouses

Hello ModalAI Engineering Team,

We are developing FloraVision AI, an agricultural computer vision platform for commercial rose production in Ecuador.

We are evaluating the Starling 2 Max for autonomous indoor flights inside commercial rose greenhouses.

Operating environment:

• Location: Tabacundo, Ecuador.
• Altitude: approximately 2,800–3,000 meters above sea level.
• GNSS-denied greenhouse environment.
• Metal structures, support wires, plastic roofing, and dense vegetation.
• Visually repetitive rows of rose plants.
• Requirement for repeatable image-acquisition missions above crops.

We would appreciate technical guidance on the following questions:

1. Which onboard localization and navigation capabilities are available for autonomous flight without GNSS?
2. How reliable is visual-inertial localization in repetitive greenhouse rows with limited visual features?
3. Can the platform execute predefined and repeatable trajectories without GPS?
4. What software components, SDKs, or additional development are required to implement those trajectories?
5. Which onboard sensors are used for obstacle detection and avoidance, and what limitations should we consider around thin wires, foliage, and greenhouse structures?
6. Does ModalAI have documentation or examples for autonomous indoor waypoint navigation using Starling 2 Max?
7. What safety measures and fallback behaviors are recommended if localization becomes unreliable?

We understand that application-specific development is our responsibility. We are seeking confirmation of the platform's existing capabilities, technical limitations, and recommended development resources.

Thank you for your guidance.

FloraVision AI Engineering Team
Ecuador

[juancho248]

Title: Starling 2 Max – GPS-Denied Autonomous Navigation Inside Rose Greenhouses

Hello ModalAI Engineering Team,

We are developing FloraVision AI, an agricultural computer vision platform for commercial rose production in Ecuador.

We are evaluating the Starling 2 Max for autonomous indoor flights inside commercial rose greenhouses.

Operating environment:

• Location: Tabacundo, Ecuador.
• Altitude: approximately 2,800–3,000 meters above sea level.
• GNSS-denied greenhouse environment.
• Metal structures, support wires, plastic roofing, and dense vegetation.
• Visually repetitive rows of rose plants.
• Requirement for repeatable image-acquisition missions above crops.

We would appreciate technical guidance on the following questions:

1. Which onboard localization and navigation capabilities are available for autonomous flight without GNSS?
2. How reliable is visual-inertial localization in repetitive greenhouse rows with limited visual features?
3. Can the platform execute predefined and repeatable trajectories without GPS?
4. What software components, SDKs, or additional development are required to implement those trajectories?
5. Which onboard sensors are used for obstacle detection and avoidance, and what limitations should we consider around thin wires, foliage, and greenhouse structures?
6. Does ModalAI have documentation or examples for autonomous indoor waypoint navigation using Starling 2 Max?
7. What safety measures and fallback behaviors are recommended if localization becomes unreliable?

We understand that application-specific development is our responsibility. We are seeking confirmation of the platform's existing capabilities, technical limitations, and recommended development resources.

Thank you for your guidance.

FloraVision AI Engineering Team
Ecuador

[Anthony Kang]

Could you send the output of voxl-version? The .ulg from the PX4 flight and the full mapper journal would also be helpful.

[wilkinsaf]

@juancho248

Hello,

1. Which onboard localization and navigation capabilities are available for autonomous flight without GNSS? -> The starling 2 max uses openvins for localization, and we would encourage you to use april tag relocalization in order to navigate around indoor environments as openvins can drift over time.
2. How reliable is visual-inertial localization in repetitive greenhouse rows with limited visual features? -> This is tough to be deterministic BUT, cant expect 2% error over distance. Greenhouse rows reminds us of the SAVLoc from Micheal Baldwin (presented at the PX4 dev summit): https://www.youtube.com/watch?v=9wk9_TNN1DA. This is not something that is present natively on the VOXL but presents an interesting localization technique indoors that you can utilize.
3. Can the platform execute predefined and repeatable trajectories without GPS? -> yes, but again we would recommend utilizing the april tags for relocalization as there is some error over distance. you can also utilize mavsdk in order to put the drone in offboard mode and control trajectories. You can find that documentation here: https://docs.modalai.com/mavsdk/
4. What software components, SDKs, or additional development are required to implement those trajectories? -> refer to answer in last question
5. Which onboard sensors are used for obstacle detection and avoidance, and what limitations should we consider around thin wires, foliage, and greenhouse structures? -> TOF module as well as tflite and opencv for computer vision. There is a collision prevention module inside PX4 that will prevent the drone from flying into something in position flight mode. You obviously have already used voxl-mapper. Anything higher level you can program in yourself in our ROS 2 plugin
6. Does ModalAI have documentation or examples for autonomous indoor waypoint navigation using Starling 2 Max? -> currently mainly the voxl-mapper. There are some custom projects that Ascend Engineering has worked on to do this
7. What safety measures and fallback behaviors are recommended if localization becomes unreliable? -> Would recommend flying in a netted & controlled area that is separate from humans. Also, have a remote control that you can switch back into manual control in order to take manual control of the drone if you feel localization is becoming unstable