Probabilistic Sensor Fusion for Robotic Perception

Overview

Modern robotic systems are equipped with diverse sensor modalities-cameras, LiDAR, radar, inertial measurement units, and ultrasonic sensors-each providing complementary information about the robot’s environment and state. The challenge of combining these heterogeneous measurements into coherent and reliable perception estimates is central to autonomous robotics. This research develops principled probabilistic frameworks for sensor fusion that enable robust estimation despite sensor noise, outliers, and model uncertainties.

Probabilistic approaches to sensor fusion provide a rigorous mathematical framework for combining measurements from multiple sensors while properly accounting for uncertainty in each sensor modality. Rather than treating sensors independently or using ad hoc combination rules, probabilistic methods enable optimal integration of information when the measurement models and uncertainties are well characterized. This includes handling multimodal distributions arising from multiple hypothesis (e.g., multiple potential object interpretations) and dynamic environments.

Our research has focused on both classical approaches, including extended and unscented Kalman filters for nonlinear estimation, and advanced methods such as particle filters for highly nonlinear systems with non-Gaussian uncertainties. We have developed computationally efficient implementations suitable for real-time robotic applications and algorithms robust to outliers and incorrect model assumptions.

Key Contributions

• Bayesian State Estimation: Extended and unscented Kalman filters for nonlinear multi-sensor fusion
• Particle Filtering: Sequential Monte Carlo methods for handling non-Gaussian, multimodal distributions
• Outlier Robustness: Probabilistic approaches for detecting and mitigating sensor outliers and faults
• Heterogeneous Sensor Integration: Principled methods for combining sensors with different noise characteristics and measurement models
• Real-Time Implementation: Computationally efficient algorithms for onboard robotic systems

Methodology

Our approach integrates probabilistic theory with practical robotic implementation:

1. Probabilistic sensor modeling capturing measurement uncertainty and sensor characteristics
2. Bayesian inference frameworks for state estimation given sensor measurements
3. Kalman filter design for linear and extended-nonlinear systems
4. Particle filter development for highly nonlinear and multi-hypothesis problems
5. Computational optimization for real-time implementation
6. Experimental validation on robotic platforms with diverse sensor suites

Applications

• Localization and Mapping: Simultaneous localization and mapping (SLAM) using diverse sensors
• Object Recognition: Integration of vision and geometric data for robust object perception
• Motion Estimation: Ego-motion and velocity estimation from multiple inertial and visual measurements
• Obstacle Detection: Combining radar, LiDAR, and camera data for robust obstacle detection
• Human-Robot Interaction: Robust estimation of human state and intent from multiple sensing modalities

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