sl-perception
8d58d5e34c
Implement multi-sensor terrain classification using RealSense D435i depth and RPLIDAR A1M8: - saltybot_terrain_classification: New ROS2 package for terrain classification - TerrainClassifier: Rule-based classifier matching depth variance + reflectance to terrain type (smooth/carpet/grass/gravel) with hysteresis + confidence scoring - DepthExtractor: Extracts roughness from depth discontinuities and surface gradients - LidarExtractor: Extracts reflectance from RPLIDAR scan intensities - terrain_classification_node: 10Hz node fusing both sensors, publishes: - /saltybot/terrain_type (JSON with type, confidence, speed_scale) - /saltybot/terrain_type_string (human-readable type) - /saltybot/terrain_speed_scale (0.0-1.0 speed multiplier for smooth/carpet/grass/gravel) Speed scales: smooth=1.0, carpet=0.9, grass=0.75, gravel=0.6 Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
30 lines
965 B
XML
30 lines
965 B
XML
<?xml version="1.0"?>
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<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
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<package format="3">
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<name>saltybot_terrain_classification</name>
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<version>0.1.0</version>
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<description>Terrain classification using RealSense depth and RPLIDAR (Issue #469)</description>
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<maintainer email="seb@example.com">SaltyLab</maintainer>
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<license>MIT</license>
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<buildtool_depend>ament_python</buildtool_depend>
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<depend>rclpy</depend>
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<depend>std_msgs</depend>
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<depend>geometry_msgs</depend>
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<depend>sensor_msgs</depend>
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<depend>message_filters</depend>
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<depend>numpy</depend>
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<depend>opencv-python</depend>
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<depend>cv_bridge</depend>
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<test_depend>ament_copyright</test_depend>
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<test_depend>ament_flake8</test_depend>
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<test_depend>ament_pep257</test_depend>
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<test_depend>python3-pytest</test_depend>
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<export>
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<build_type>ament_python</build_type>
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</export>
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</package>
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