A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization

Abstract

Multi-sensor perception have an important role in robotics and autonomous systems, as inputs for critical functions such as obstacle detection, localization, etc. This Multi-sensor perception begins to appear in critical applications, such as drones and ADAS (Advanced Driver Assistance Systems). However such complex systems are dicult to validate entirely. In this paper we study these systems under an alternative dependability method: fault tolerance. We propose an approach to tolerate faults in multi-sensor data fusion based on the more classical method of duplication-comparison, and oering detection and recovery services. We detail an example implementation using Kalman lters data fusion for mobile robot localization. We demonstrate its eectiveness in this case study using real data and fault injection.

Proactive Middleware for Fault Detection and Advanced Conflict Handling in Sensor Fusion

“…This solution managed to correctly identify situations in which sensors were failing but did not yet take sensor noise into account. In [2] the authors propose a fault-tolerant architecture for sensor fusion using Kalman filters for mobile robot localization. The detection rate of the faults injected was 100%, however, the diagnosis and recovery rate is lower at 60%.…”

Proactive Model for Handling Conflicts in Sensor Data Fusion Applied to Robotic Systems

“…This solution managed to correctly identify situations in which sensors were failing but did not yet take sensor noise into account. In (Bader et al, 2017) the authors propose a fault tolerant architecture for sensor fusion using Kalman filters for mobile robot localization. The detection rate of the faults injected was 100%, however the diagnosis and recovery rate is lower at 60%.…”

A Safety Monitoring Model for a Faulty Mobile Robot

“…It also points out a validation problem, not only because of the dynamic environment of robots creating constant unforeseeable scenarios, but also because the programming is usually done with a declarative paradigm that is harder to validate and not recommended by a European standard in what concerns critical systems. Despite the warnings, this technique does show that redundancy is continuously used, as the fusion consists of two different blocks; each fusion offers different sensor information, with fault detection being achieved by comparing both block outputs [27].…”