Week 06 | Day 07

Week 6 Summary: Sensors, Perception, and SLAM

Published: April 27, 2026 | Author: Smartotics Learning Journey | Reading Time: 12 min

TL;DR: Week 6 covered the full perception stack: sensor types (vision/LiDAR/IMU/tactile), computer vision (YOLO/tracking/pose), LiDAR processing (registration/segmentation), SLAM (ORB-SLAM3/VINS-Mono), calibration (hand-eye/temporal), and a complete multi-sensor pipeline implementation.

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📚 Week 6 At a Glance

Day	Topic	Key Skills	Code
D1	Sensor Overview	Sensor taxonomy, selection matrix	IMU drift demo
D2	Computer Vision	YOLO detection, Kalman tracking, ArUco pose	Full vision pipeline
D3	LiDAR Processing	ICP, RANSAC, DBSCAN, mapping	Open3D pipeline
D4	SLAM	ORB features, IMU preintegration, loop closure	Feature tracking
D5	Calibration	Camera intrinsics, hand-eye, temporal sync	Calibration toolkit
D6	Python Practice	Multi-sensor fusion pipeline	Integrated system
D7	This Summary	Recap, connections, preview	—

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🎯 Core Concepts

The Perception Stack

Raw Sensors → Preprocessing → Detection/Tracking → Fusion → Decision

Layer	Function	Key Algorithms
Sensing	Capture physical world	Camera, LiDAR, IMU, tactile
Preprocessing	Clean and reduce data	Voxel filter, undistort, denoise
Detection	Find objects of interest	YOLO, RANSAC plane extraction
Tracking	Maintain identity over time	Kalman, DeepSORT, ByteTrack
Pose Estimation	Get 3D position	PnP, ArUco, ICP
Fusion	Combine sensor outputs	EKF, BEVFusion, manual association
Mapping	Build persistent representation	Octree, voxel grid, pose graph

Key Equations

Camera Projection:

p = K * [R|t] * P
pixel = intrinsics @ extrinsics @ world_point

ICP Registration:

minimize Σ ‖(R*p_i + t) - q_j‖²
where q_j = nearest_neighbor(R*p_i + t)

Extended Kalman Filter:

Predict: x̂ = f(x, u), P = F*P*Fᵀ + Q
Update: K = P*Hᵀ*(H*P*Hᵀ + R)⁻¹
        x = x̂ + K*(z - h(x̂))
        P = (I - K*H)*P

Hand-Eye Calibration:

A * X = X * B
robot_movement * hand_eye = hand_eye * camera_movement

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🔑 Key Takeaways by Day

Day 1: Sensors

• No sensor is perfect: Cameras fail in darkness; LiDAR fails in fog; IMU drifts
• Multi-sensor fusion is mandatory: Camera (rich info) + LiDAR (precise depth) + IMU (high-rate)
• Cost-performance is shifting: Solid-state LiDAR dropped from $75K to $400

Day 2: Computer Vision

• YOLO is the practical detection choice: 100+ FPS, good accuracy, massive ecosystem
• Tracking is harder than detection: Maintaining identity across occlusions requires careful algorithm selection
• ArUco markers solve pose practically: 1mm accuracy, 1ms computation, no training required

Day 3: LiDAR Processing

• Preprocessing is essential: Voxel downsampling + outlier removal are mandatory first steps
• Ground extraction simplifies everything: RANSAC plane fitting reduces planning complexity by 80%+
• ICP requires good initialization: Real systems use odometry for initial guess

Day 4: SLAM

• SLAM is the chicken-and-egg problem solved: Frontend tracks; backend optimizes; loop closure eliminates drift
• Visual-Inertial achieves <0.5% drift: Camera + IMU = metric scale + rich information
• ORB-SLAM3 is the reference implementation: Mono/stereo/RGB-D + IMU, multi-map recovery

Day 5: Calibration

• Calibration is the invisible foundation: 30% of sensor integration time should be spent on calibration
• Checkerboard + OpenCV solves 90%: 20-40 images, <0.5 px reprojection error
• Hand-eye requires motion diversity: Pure translation or rotation causes degeneracy

Day 6: Python Practice

• Modularity enables testing: Each sensor module independently developable
• Timestamp synchronization is critical: Camera (30 Hz) and LiDAR (10 Hz) need temporal alignment
• This pipeline is ROS2-ready: Structure maps directly to ROS2 nodes

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⚠️ Common Mistakes

Mistake	Why It Happens	How to Avoid
Using raw IMU without bias removal	Assuming IMU is perfectly zeroed	Always calibrate static bias before use
Forgetting to undistort images	Using factory distortion parameters	Run checkerboard calibration for your specific lens
ICP without initialization	Assuming scans are close to aligned	Use odometry or feature-based coarse alignment first
Ignoring temporal sync	Assuming all sensors share a clock	Hardware trigger or software interpolation mandatory
Single-sensor reliance	Cost or simplicity	Always have sensor redundancy for safety-critical systems
Poor hand-eye motion diversity	Convenient movements only	Collect samples with varying orientation AND translation

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🔮 Preview: Week 7 — ROS2 Introduction

Week 7 transitions from algorithms to integration:

Day	Topic	What You’ll Learn
D1	ROS2 Fundamentals	Nodes, topics, messages, pub/sub
D2	Sensor Drivers	camera_driver, lidar_driver, IMU driver
D3	Perception Node	Wrap Week 6 pipeline into ROS2 node
D4	TF2 Transforms	Coordinate frame management
D5	RViz Visualization	Real-time sensor visualization
D6	Launch Files	Multi-node orchestration
D7	Week 7 Summary	Full ROS2 perception system

The goal: Take the Week 6 Python pipeline and deploy it as a production-ready ROS2 system with logging, parameter configuration, and distributed node architecture.

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📚 Recommended Next Steps

1. Run the Week 6 code: Execute all Python examples on your own hardware (webcam + optional LiDAR)
2. Calibrate your camera: Use the Day 5 calibration toolkit with a printed checkerboard
3. Experiment with YOLO: Train on custom objects (your robot’s operational environment)
4. Try ORB-SLAM3: Download and run on your camera feed to see real-time SLAM

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🔗 Connections to Previous Weeks

• Week 2 (Kinematics): Forward/inverse kinematics → Camera projection model (Day 2)
• Week 3 (Path Planning): Configuration space → Ground/obstacle segmentation (Day 3)
• Week 5 (Control): Dynamics → IMU measurements and state estimation (Day 1)
• Week 6 (Perception): This week — the bridge between sensors and action

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