This paper describes a dataset collected along a 1 km section of beach near Katwijk, The Netherlands, which was populated with a collection of artificial rocks of varying sizes to emulate known rock size densities at current and potential Mars landing sites. First, a fixed-wing unmanned aerial vehicle collected georeferenced images of the entire area. Then, the beach was traversed by a rocker-bogie-style rover equipped with a suite of sensors that are envisioned for use in future planetary rover missions. These sensors, configured so as to emulate the ExoMars rover, include stereo cameras, and time-of-flight and scanning light-detection-and-ranging sensors. This dataset will be of interest to researchers developing localization and mapping algorithms for vehicles traveling over natural and unstructured terrain in environments that do not have access to the global navigation satellite system, and where only previously taken satellite or aerial imagery is available.
Mobile robots should possess accurate self-localization capabilities in order to be successfully deployed in their environment. A solution to this challenge may be derived from visual odometry (VO), which is responsible for estimating the robot's pose by analysing a sequence of images. The present paper proposes an accurate, computationally-efficient VO algorithm relying solely on stereo vision images as inputs. The contribution of this work is twofold. Firstly, it suggests a non-iterative outlier detection technique capable of efficiently discarding the outliers of matched features. Secondly, it introduces a hierarchical motion estimation approach that produces refinements to the global position and orientation for each successive step. Moreover, for each subordinate module of the proposed VO algorithm, custom non-iterative solutions have been adopted. The accuracy of the proposed system has been evaluated and compared with competent VO methods along DGPS-assessed benchmark routes. Experimental results of relevance to rough terrain routes, including both simulated and real outdoors data, exhibit remarkable accuracy, with positioning errors lower than 2%.
Mars exploration is expected to remain a focus of the scientific community in the years to come. A Mars rover should be highly autonomous because communication between the rover and the terrestrial operation center is difficult, and because the vehicle should spend as much of its traverse time as possible moving. Autonomous behavior of the rover implies that the vision system provides both a wide view to enable navigation and three‐dimensional (3D) reconstruction, and at the same time a close‐up view ensuring safety and providing reliable odometry data. The European Space Agency funded project “SPAring Robotics Technologies for Autonomous Navigation” (SPARTAN) aimed to develop an efficient vision system to cover all such aspects of autonomous exploratory rovers. This paper presents the development of such a system, starting from the requirements up to the testing of the working prototype. The vision system was designed with the intention of being efficient, low‐cost, and accurate and to be implemented using custom‐designed vectorial processing by means of field programmable gate arrays (FPGAs). A prototype of the complete vision system was developed, mounted on a basic mobile robot platform, and tested. The results on both real‐world Mars‐like and long‐range simulated data are presented in terms of 3D reconstruction and visual odometry accuracy, as well as execution speed. The developed system is found to fulfill the set requirements.
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