feat(lunar_sim): add scene mast, boulders, and A300 lidars - #945
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WalkthroughThe lunar simulation adds front and rear image-based lidars, a visible camera mast, four route boulders, lidar topics, collision validation, sensor checks, and inspection documentation. ChangesLunar simulation sensors and obstacles
Priority: ⬇️ Low Merge Risk: ⚪ Minimal · up to The simulation adds the documented sensors, boulders, and validation artifacts without an established merge-blocking risk. 🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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In `@src/lunar_sim/description/validate_and_render.py`:
- Around line 147-153: Update dead_reckon_square’s boulder identification to
require all four expected boulder geoms via require_id, or assert the discovered
names exactly match the expected set, before step_clear_route validation; do not
allow missing or renamed geoms to produce an incomplete boulder_ids set.
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src/lunar_sim/README.mdsrc/lunar_sim/description/assets/boulders_provenance.txtsrc/lunar_sim/description/validate_and_render.py
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…-D Pro Stars, a bumpy horizon and long lunar shadows for the ROSCon dead-reckoning video, plus the OAK-D Pro that shows them off and that the visual-odometry work needs anyway. Star field skybox, from generate_starfield.py. The gradient skybox it replaces rendered as featureless near-black; MuJoCo builtins cannot draw stars. Stars are sampled on the sphere and then projected onto a cube map, which keeps them continuous across the seams. Far-field horizon, from generate_far_terrain.py. Visual only, so the driven terrain keeps the 10 m half-extents its spawn elevation, rock keepout, square calibration and shadow frustum all derive from. It has to meet that terrain's rim rather than sit beyond it: from the camera's height the rim already is the horizon, so ridges with void behind it render as black sky below the horizon line. The join follows Chebyshev distance, since the driven hfield is square and its corners reach 14.1 m. That field also forces <statistic extent="28.29"> to be pinned. MuJoCo derives extent from the model's bounding volume and scales the camera near/far planes and shadow frustum by it, and the horizon inflates the derived extent from 28.29 to 849 m. Unpinned, the near plane moves 0.28 -> 8.49 m, which would blind the lidars merged in #945 - their documented 0.28 m minimum range is this figure. Sun lowered to ~28 deg for long shadows, with diffuse raised to 1.85 to restore the 2x ground illumination that costs; at the inherited 0.9 the ground sat at 62/255 and the shadows were not visible. shadowclip 5.0 -> 0.6, the tightest value still covering every caster, which measured smoothest and resolved ~3% more shadow. Ground split into collision and visual geoms. MuJoCo renders and collides one hfield mesh, and the driven field's 2.0M triangles cost 134 ms per camera frame - about 2.5 fps over three cameras, too choppy to record. That field cannot be shrunk: the closure error is calibrated against it, and a 350x350 collision surface moved that error from 0.6331 m to 1.8232 m. So it keeps full resolution in geom group 3, which MuJoCo does not render, and generate_visual_terrain.py's downsampled twin draws in its place. 5.3 ms per frame, settled height and closure error bit-identical, mean rendered pixel difference 1.32/255. The README records which sensors that split affects. Anything measuring the rendered surface reads the twin, within 0.49 mm mean and 26.9 mm worst case on a crater rim inside the route. #945's lidars are depth cameras rather than <rangefinder> sensors, so they stay consistent with every other camera; a native rangefinder would instead see both surfaces, because MuJoCo raycasts with geomgroup=NULL and collision masks only suppress contacts. OAK-D Pro on the arch's real front camera mount, composed from upstream's amp_sensor_arch.urdf.xacro and the arch's offset from chassis_link. fovy and the 0.075 m baseline are the profile hangar_sim pins, so captures stay comparable for the visual-odometry work this camera exists for. The stereo pair is gated behind enable_vo, default off; oakd_color is not. render_publish_rate 10 -> 30, now that frames are cheap enough to meet it. #945's measured sensor rates predate both that and the ground split, so the README flags them as due a re-measurement. Tests cover the mount pose against the vendored xacro, the stereo baseline, the no-occlusion margin, the optical-frame site every fixed camera needs, the far field staying under the driven ground, and the split keeping the driven field collision-only with a visual twin that still matches it. That last check compares both heightmaps on their shared 8-bit full scale rather than self-normalising each, so a twin with the right shape but scaled amplitude cannot pass - self-normalising accepted one at 0.80x. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
bkanator
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…-D Pro Stars, a bumpy horizon and long lunar shadows for the ROSCon dead-reckoning video, plus the OAK-D Pro that shows them off and that the visual-odometry work needs anyway. Star field skybox, from generate_starfield.py. The gradient skybox it replaces rendered as featureless near-black; MuJoCo builtins cannot draw stars. Stars are sampled on the sphere and then projected onto a cube map, which keeps them continuous across the seams. Far-field horizon, from generate_far_terrain.py. Visual only, so the driven terrain keeps the 10 m half-extents its spawn elevation, rock keepout, square calibration and shadow frustum all derive from. It has to meet that terrain's rim rather than sit beyond it: from the camera's height the rim already is the horizon, so ridges with void behind it render as black sky below the horizon line. The join follows Chebyshev distance, since the driven hfield is square and its corners reach 14.1 m. That field also forces <statistic extent="28.29"> to be pinned. MuJoCo derives extent from the model's bounding volume and scales the camera near/far planes and shadow frustum by it, and the horizon inflates the derived extent from 28.29 to 849 m. Unpinned, the near plane moves 0.28 -> 8.49 m, which would blind the lidars merged in #945 - their documented 0.28 m minimum range is this figure. Sun lowered to ~28 deg for long shadows, with diffuse raised to 1.85 to restore the 2x ground illumination that costs; at the inherited 0.9 the ground sat at 62/255 and the shadows were not visible. shadowclip 5.0 -> 0.6, the tightest value still covering every caster, which measured smoothest and resolved ~3% more shadow. Ground split into collision and visual geoms. MuJoCo renders and collides one hfield mesh, and the driven field's 2.0M triangles cost 134 ms per camera frame - about 2.5 fps over three cameras, too choppy to record. That field cannot be shrunk: the closure error is calibrated against it, and a 350x350 collision surface moved that error from 0.6331 m to 1.8232 m. So it keeps full resolution in geom group 3, which MuJoCo does not render, and generate_visual_terrain.py's downsampled twin draws in its place. 5.3 ms per frame, settled height and closure error bit-identical, mean rendered pixel difference 1.32/255. The README records which sensors that split affects. Anything measuring the rendered surface reads the twin, within 0.49 mm mean and 26.9 mm worst case on a crater rim inside the route. #945's lidars are depth cameras rather than <rangefinder> sensors, so they stay consistent with every other camera; a native rangefinder would instead see both surfaces, because MuJoCo raycasts with geomgroup=NULL and collision masks only suppress contacts. OAK-D Pro on the arch's real front camera mount, composed from upstream's amp_sensor_arch.urdf.xacro and the arch's offset from chassis_link. fovy and the 0.075 m baseline are the profile hangar_sim pins, so captures stay comparable for the visual-odometry work this camera exists for. The stereo pair is gated behind enable_vo, default off; oakd_color is not. render_publish_rate 10 -> 30, now that frames are cheap enough to meet it. #945's measured sensor rates predate both that and the ground split, so the README flags them as due a re-measurement. Tests cover the mount pose against the vendored xacro, the stereo baseline, the no-occlusion margin, the optical-frame site every fixed camera needs, the far field staying under the driven ground, and the split keeping the driven field collision-only with a visual twin that still matches it. That last check compares both heightmaps on their shared 8-bit full scale rather than self-normalising each, so a twin with the right shape but scaled amplitude cannot pass - self-normalising accepted one at 0.80x. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Motivation
Give the lunar demo visible terrain landmarks and rover-mounted lidar data for perception experiments.
Brief description
Adds a world-fixed mast under the existing scene camera, four large boulders, and front/rear depth-camera lidars on the Husky A300. The README records sensor topics, mount offsets, asset provenance and measured motion results.
(0.45, 0, 0.3208593)and(-0.2728, 0, 0.6500193)metres.811 x 3output currently has one usable horizontal row because upstream projection drops the outer rows. Effective minimum range is about 0.28 m due to rendering near clipping; maximum range is 25 m.How it was tested
picknik_mujoco_ros10.2.0 package and confirmedTHREE_D_LIDAR = 2support before configuring the lidars.moveit_pro build user_workspace -w "$PWD" --colcon-args "--packages-up-to lunar_sim": 3 packages built successfully.pytest src/lunar_sim/test/test_husky_mujoco_geometry.py: 4 passed.colcon test --packages-select lunar_sim lunar_sim_behaviors: all 7 CTest targets passed, with no errors or failures.validate_and_render.pypassed with GPU EGL rendering. Camera/site transforms match, and the square recorded zero boulder contacts. Physical before/after results match, including 0.6290 m closure error. This preserves the existing open-loop behavior; it does not claim accurate square tracking.ros2 topic hzandros2 topic echo --oncefor/scene_camera/color,/scene_camera/depth,/lidar_front/pointsand/lidar_rear/points. Also checked/scene_camera/camera_infoand both optical-frame transforms. Measured about 5.6 Hz scene images, 7.2 Hz front lidar and 6.0 Hz rear lidar, against configured 10 Hz rates.Dead Reckon Squaresuccessfully, recording 7.3695 m wheel-odometry travel and 6.3654 rad accumulated yaw. Teleoperation forwarded all 40 test commands, recorded 0.90475 m odometry displacement and ended with zero commanded twist.validate_and_render.pyon the combined scene: it passed with zero boulder contacts, the same 0.6290 m closure error and per-corner turns, and matching camera/site transforms. The only rebase conflict was the two scene include lines; both sides were kept.pre-commit run --filesfor the README, provenance note and validation script. Automated PR checks passed before squashing and are rerunning on the final commit.Release notes
lunar_sim.Claude agent checks
Reviewed commit
2e1842d0cc037bd11d019b28b9bc0e6791f254eausing the MoveIt Pro review prompts.code-reviewerdocumentation-botlicensing-privacy-botplatform-architect-botroboticist-botfrontend-noah-botsecurity-auditorcompatibility-botsonar-bottest-runner