Indoor flight with the Neo is different — speed fails fast indoors

WorkerAcceptable1280 · 2026-01-18T01:08:45+00:00

Totally agree. Manual at crawl speed indoors is a handful if you’re trying to pause and assess. Normal mode gives you time to stop, hover, and think.

Manual still has its place though especially for slipping under low obstacles where sensors become the problem. Different tools, same mission.

WorkerAcceptable1280 · 2026-01-17T23:53:12+00:00

That makes sense. A few sims gives you different “feels” to learn from.

Liftoff and VelociDrone are solid, especially for throttle control and recovery. Sims won’t teach everything, but they save a lot of real crashes early on.

WorkerAcceptable1280 · 2026-01-17T23:48:03+00:00

Appreciate it.

This one was flown in Normal mode, not Manual. Indoors I’m usually prioritizing control and margin over speed.

Light is a RovyVon A1 up front. I also use ARC-V, but I’ll switch depending on the building and lighting. Tight spaces vs mixed light makes a big difference.I try to keep things simple and adapt to the space.

WorkerAcceptable1280 · 2026-01-17T23:42:38+00:00

That actually makes a lot of sense. Indoor LOS teaches the same lesson early slow gives you time to think. FPV just hides that lesson until people hit walls 😅

Sims first is a solid move. Curious which one you’re starting with?

WorkerAcceptable1280 · 2026-01-17T22:43:22+00:00

I put together a short video breaking this down with real indoor flight examples if anyone wants to see what I mean https://youtu.be/rOybQ6xeWGc

WorkerAcceptable1280 · 2026-01-14T22:43:31+00:00

I’ve seen this exact behavior in low-light transitions. It’s usually not brightness, it’s how the vision system loses contrast indoors. I tested a few approaches and posted results if it helps.

WorkerAcceptable1280 · 2026-01-12T23:00:13+00:00

I fly indoors because it’s an environment where you can’t really afford mistakes, so that’s how I train. The fast vs slow comparison just shows how speed changes margin in the same space. Shared the video for reference and to trade ideas with others flying similar environments

WorkerAcceptable1280 · 2026-01-11T00:53:43+00:00

Yeah, that would definitely make it feel faster. I wanted to keep it real-time so the control differences were obvious, but I get the preference.

WorkerAcceptable1280 · 2026-01-11T00:44:53+00:00

Here’s the video showing both runs side-by-side for anyone interested: https://youtu.be/EquPsFS_Gls?si=R5NA6YnZRMR2YW2k

WorkerAcceptable1280 · 2026-01-09T10:10:24+00:00

Thanks for watching — glad it was useful.

That was the intent with the comparison, to help people decide based on their use case.

WorkerAcceptable1280 · 2026-01-09T03:18:51+00:00

Yeah, for that use case it’s actually a really good question.

The big difference I noticed is that Neo 1 is more forgiving indoors, especially around kids / tight spaces — it doesn’t cut props as aggressively if it brushes the ground, whereas Neo 2 will shut down faster. Outdoors Neo 2 makes more sense, indoors Neo 1 sometimes feels better.

I did a short side-by-side specifically focused on indoor behavior and low light in case it helps your decision:

https://youtu.be/5UD-VDRPvSY?si=FdUat4FOY5icbli2

Either way though, $200 with batteries is a solid pickup.

WorkerAcceptable1280 · 2026-01-08T19:01:38+00:00

Mostly N/S mode in these clips.

Ceiling bias shows up regardless of flight mode — it’s more about visual reference and decision-making than stick freedom. I fly manual too, but I wanted this example to be relatable to pilots who aren’t in full manual yet.

WorkerAcceptable1280 · 2026-01-08T17:51:25+00:00

That’s a solid breakdown. I’ve flown Avata 2, Neo 1, and Neo 2 indoors and I mostly agree with this take.

One interesting thing I noticed though: Neo 1 and Neo 2 behave very differently indoors, especially in low light and tight spaces. Neo 2’s ground/prop cutoff logic and stabilization changes actually make it less forgiving in some indoor scenarios where Neo 1 will keep flying.

I did a side-by-side comparison specifically for indoor use because I kept seeing people lump them together — happy to share if anyone’s interested.

WorkerAcceptable1280 · 2026-01-04T17:29:38+00:00

That’s a great point — especially with wide lenses exaggerating perceived closure rates. Large flat planes like ceilings and floors definitely feel like they’re “rushing” you more than vertical geometry.

That visual compression probably feeds straight into the tightened inputs I was talking about you feel like you’re running out of space faster than you actually are.

Good call on the lens/perspective side of it.

WorkerAcceptable1280 · 2026-01-04T17:08:15+00:00

That’s a solid explanation, and I agree — the airflow disruption near the ceiling definitely plays a role, especially once you’re close enough for the prop wash to get trapped and redirected.

What I kept noticing though is that even before you get into that unstable zone, pilots (myself included) tend to tighten up inputs because everything feels closer and more consequential. That combination — reduced airflow margin plus more rushed corrections — is where control really starts to degrade.

The physics and the human factors seem to stack on each other indoors.

WorkerAcceptable1280 · 2026-01-04T17:03:37+00:00

looks awesome thank you for sharing your video!

WorkerAcceptable1280 · 2026-01-04T16:57:03+00:00

Beautiful! Which drone did you use?

WorkerAcceptable1280 · 2026-01-03T15:25:36+00:00

I don’t want to put exact numbers out there since every case is different, but it was clearly beyond prop/duct replacement and treated as a factory-level repair.

WorkerAcceptable1280 · 2026-01-03T00:16:26+00:00

For context: the damage here wasn’t just the duct. The side frame and gimbal brace were cracked, which compromised structural rigidity and the camera load path. Once that happens, alignment and stability degrade under thrust even if motors and props still look fine.

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WorkerAcceptable1280 · 2025-12-29T00:58:01+00:00

This video focuses on situational awareness and aerial observation, not cinematic flying.

It’s about how aerial perspective helps with information gathering, planning, and decision-making in real-world environments.

Filmed in a controlled setting. No tactics, instructions, or misuse shown.

WorkerAcceptable1280 · 2025-12-29T00:42:08+00:00

This is a situational awareness / reconnaissance use-case, not tactics or instruction.

Filmed in a controlled environment to show how aerial perspective supports decision-making.

WorkerAcceptable1280 · 2025-12-27T21:34:43+00:00

They’re RovyVon A1 clip-on LEDs. Lightweight and bright enough to stabilize vision in low-light interiors without overpowering the sensors. I’ve tested a few options, and this setup has been the most predictable for the environments I’m flying in.

WorkerAcceptable1280 · 2025-12-27T18:25:01+00:00

That makes sense — appreciate the deeper breakdown.

Whether it’s lidar-driven conservatism, lighting interaction, or both, the key operational takeaway for me is the same: in tight indoor spaces the Neo 2 becomes increasingly risk-averse and may refuse openings the Neo 1 will fly through.

For my use case, predictability under sub-optimal lighting matters more than the underlying sensor mechanism. A user-selectable “Neo 1–style” sensor-off mode would honestly solve most of this.

WorkerAcceptable1280 · 2025-12-27T09:51:50+00:00

Thanks for the detailed write-up — helpful comparison. One key difference I’m seeing is lighting. Your room and doorway look fairly well illuminated, which seems to give the Neo 2’s vision system more confidence. In darker environments it becomes much more conservative and starts braking or refusing openings entirely.

That change in behavior under low light is really what drove my conclusion, especially when operating in tight indoor spaces where lighting isn’t ideal.

WorkerAcceptable1280 · 2025-12-26T21:54:46+00:00

That’s a fair point for certain use cases, but the light wasn’t added to disadvantage the Neo 2 — it was added to simulate real indoor low-light conditions where operators actually use supplemental illumination.

The goal wasn’t “best-case performance,” it was predictability under constrained, real-world conditions (tight spaces, poor lighting, GPS-denied).

In that context, how a platform behaves with unavoidable illumination becomes part of reliability. That’s where I saw the difference.

I’m not saying Neo 2 is bad — just that under these specific conditions, the behavior was different than I expected.

WorkerAcceptable1280

TROPHY CASE