Configuring Lidar Sensors

Configuring Lidar Sensors

When to Use

Use this skill when the user asks to create a lidar scene, configure an OmniLidar prim, choose lidar output frame/coordinate behavior, or request lidar PointCloud channels.

Inputs

Resolve inputs in this order: existing repository files and referenced snippets, explicit user request, then broader agent context.

• Target API surface: Python, C/C++, USD, or a combination.
• Lidar sensor prim path, pose, frame rate, sensor model attributes, and optional emitter-state requirements.
• RenderProduct path, PointCloud RenderVar path, requested output channels, coordinate representation, and output frame.
• Whether the user needs USD authoring only or an end-to-end example that steps and reads the configured output.
• Repository source snippets referenced below. Treat these snippets as the API source of truth.

Prerequisites

• Use an ovrtx checkout that contains the referenced examples and docs tests.
• Read the relevant > **Source:** snippet before writing or explaining API usage.
• Use reading-sensor-pointclouds for map/read code after the configured scene renders.
• Use interpreting-lidar-pointclouds for channel units, validity, and visualization semantics.

Instructions

1. Identify the lidar prim, RenderProduct, PointCloud RenderVar, requested channels, coordinate behavior, and output frame.
2. Read the matching USD, Python, or C/C++ source snippet before writing API usage.
3. Author the OmniLidar prim and PointCloud RenderVar channel set explicitly; do not imply channels are available unless requested or auto-enabled.
4. Combine configuration, reading, and interpretation skills only when the user asks for an end-to-end lidar workflow.
5. When changing code, run the narrow docs test or example that owns the snippet whenever practical.

Output Format

• For explanations, cite the relevant API names, source snippets, and caveats.
• For code changes, summarize the files changed, snippets affected, and validation run.

Scripts

This skill has no scripts.

Limitations

• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

An ovrtx lidar setup has two parts:

• An OmniLidar prim with OmniSensorGenericLidarCoreAPI that defines the sensor model, pose, frame rate, and output behavior.
• A RenderProduct pointing at that lidar plus a PointCloud RenderVar that selects the lidar output channels to expose.

The same USDA scene pattern is used from Python and C; the language-specific code only loads the scene, steps the render product, and maps the output.

Minimal Lidar Prim

Python example scene

Source: examples/python/lidar/lidar_example.usda snippet configure-lidar-sensor

C example scene

Source: examples/c/lidar/lidar_example.usda snippet configure-lidar-sensor

Use OmniSensorGenericLidarCoreAPI for the generic lidar model. Add emitter-state API schemas only when authoring custom firing patterns with explicit emitter state arrays.

In Z-up scenes, keep the physical scene Z-up and rotate the lidar prim so sensor +X points along the intended forward direction. Avoid changing the output frame just to satisfy visualization; choose the frame based on the data contract the application needs.

Output Attributes

These attributes, among others, live on the lidar prim with the omni:sensor:Core: prefix.

OmniLidar prims expose many additional sensor, scan, and emitter attributes for range, angular coverage, resolution, timing, firing patterns, and model-specific behavior. Preserve existing attributes when editing a tuned sensor, and author model-specific values from the schema when the example defaults are not enough.

For the full schema-derived attribute list, read schemas/omni_sensors/schema.usda.

Output Behavior

PointCloud Render Output

Python example scene

Source: examples/python/lidar/lidar_example.usda snippet configure-lidar-pointcloud-output

C example scene

Source: examples/c/lidar/lidar_example.usda snippet configure-lidar-pointcloud-output

sourceName = "PointCloud" is the lidar sensor render output. Request only the channels needed by the consumer to keep memory use down.

Common lidar channels:

Troubleshooting

• Output-defining attributes are not intended for runtime mutation; author them in USD before loading the scene.
• PointCloud contains requested payload channels plus model-added Counts and Flags. If a downstream reader expects Intensity or TimeOffsetNs, include those names in token[] channels.
• Counts defines the delivered range in per-point tensors. Do not iterate over the full tensor allocation.
• If includeInvalidPoints = true, Counts bounds the delivered entries, but entries inside that range can still be invalid; test Flags[i] & VALID before consuming point attributes that require a real return.
• MotionBVH is required for correct lidar motion effects. Static scenes may run without it, but moving objects or motion compensation need MBVH enabled through the renderer configuration.
• Lidar output frame and visualizer frame are separate concerns. If output is in SENSOR frame, configure the visualizer to interpret that frame instead of changing the sensor to WORLD unless world-frame output is actually desired.
• The model auto-enables Flags and Counts even when not requested, and delivers them like ordinary channel tensors.

Related Skills

• reading-render-output for mapping render variables and composite tensors.
• reading-sensor-pointclouds for point-cloud tensor access patterns.
• interpreting-lidar-pointclouds for lidar channel units and visualization.

References

• Use the > **Source:** directives in this skill to locate tested snippets before reusing API patterns.
• Keep related skills, docs, and snippets synchronized when changing the workflow.