Toward an Autonomous Lunar Landing Based on Low-Speed Optic Flow Sensors

International audienceFor the last few decades, growing interest has returned to the quite chal-lenging task of the autonomous lunar landing. Soft landing of payloads on the lu-nar surface requires the development of new means of ensuring safe descent with strong final conditions and aerospace-related constraints in terms of mass, cost and computational resources. In this paper, a two-phase approach is presented: first a biomimetic method inspired from the neuronal and sensory system of flying insects is presented as a solution to perform safe lunar landing. In order to design an au-topilot relying only on optic flow (OF) and inertial measurements, an estimation method based on a two-sensor setup is introduced: these sensors allow us to accu-rately estimate the orientation of the velocity vector which is mandatory to control the lander's pitch in a quasi-optimal way with respect to the fuel consumption. Sec-ondly a new low-speed Visual Motion Sensor (VMS) inspired by insects' visual systems performing local angular 1-D speed measurements ranging from 1.5 • /s to 25 • /s and weighing only 2.8 g is presented. It was tested under free-flying outdoor conditions over various fields onboard an 80 kg unmanned helicopter. These pre-liminary results show that the optic flow measured despite the complex disturbances encountered closely matched the ground-truth optic flow

Experimental Evaluation of a Visual-Inertial Navigation System with Guaranteed Convergence

This contribution presents a constraints-based loosely-coupled Augmented Implicit Kalman Filter approach to vision-aided inertial navigation that uses epipolar constraints as output map. The proposed approach is capable of estimating the standard navigation output (ve- locity, position and attitude) together with inertial sensor biases. An observability analysis is proposed in order to define the motion requirements for full observability of the system and asymptotic convergence of the parameter estimates. Simulations and experimental results are summarized that confirm the theoretical conclusions

Evaluating the effect of robot group size on relative localisation precision

Looking on co-operative position estimation in multi-robot systems, the question to what extend the number of robots has an influence on the quality of the resulting localisation is an important and interesting issue. This paper addresses this relation regarding a pure relative localisation approach based only on mutual observations between the robots. The intuitive expectation that more robots should improve the position estimation is motivated and the design of the experiments with special respect to possibly distorting parameters is discussed and reasoned in detail. An in-depth analysis of the collected data explains the only partial conformance of the experimental results with the expected outcome

On the consistency of Vision-aided Inertial Navigation

Abstract In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS). We show that standard (linearized) estimation approaches, such as the Extended Kalman Filter (EKF), can fundamentally alter the system observability properties, in terms of the number and structure of the unobservable directions. This in turn allows the influx of spurious information, leading to inconsistency. To address this issue, we propose an Observability-Constrained VINS (OC-VINS) methodology that explicitly adheres to the observability properties of the true system. We apply our approach to the Multi-State Constraint Kalman Filter (MSC-KF), and provide both simulation and experimental validation of the effectiveness of our method for improving estimator consistency.

VSO: Visual Semantic Odometry

ISSN:0302-9743ISSN:1611-334