This paper introduces a "weighted" matching algorithm to estimate a robot's planar displacement by matching twodimensional range scans. The influence of each scan point on the overall matching error is weighted according to its uncertainty. We develop uncertainty models that account for effects such as measurement noise, sensor incidence angle, and correspondence error. Based on models of expected sensor uncertainty, our algorithm computes the appropriate weighting for each measurement so as to optimally estimate the displacement between two consecutive poses. By explicitly modeling the various noise sources, we can also calculate the actual covariance of the displacement estimates instead of a statistical approximation of it. A realistic covariance estimate is necessary for further combining the pose displacement estimates with additional odometric and/or inertial measurements within a localization framework [1]. Experiments using a Nomad 200 mobile robot and a Sick LMS-200 laser range finder illustrate that the method is more accurate than prior techniques.

Europa is a premier target for advancing both planetary science and astrobiology, as well as for opening a new window into the burgeoning field of comparative oceanography. The potentially habitable subsurface ocean of Europa may harbor life, and the globally young and comparatively thin ice shell of Europa may contain biosignatures that are readily accessible to a surface lander. Europa’s icy shell also offers the opportunity to study tectonics and geologic cycles across a range of mechanisms and compositions. Here we detail the goals and mission architecture of the Europa Lander mission concept, as developed from 2015 through 2020. The science was developed by the 2016 Europa Lander Science Definition Team (SDT), and the mission architecture was developed by the preproject engineering team, in close collaboration with the SDT. In 2017 and 2018, the mission concept passed its mission concept review and delta-mission concept review, respectively. Since that time, the preproject has been advancing the technologies, and developing the hardware and software, needed to retire risks associated with technology, science, cost, and schedule.

This paper describes the implementation and testing of Alice, the California Institute of Technology's entry in the 2005 DARPA Grand Challenge. Alice utilizes a highly networked control system architecture to provide high performance autonomous driving in unknown environments. Innovations include a vehicle architecture designed for efficient testing in harsh environments, a highly sensory‐driven approach to fuse sensor data into speed maps used by real‐time trajectory optimization algorithms, health and contingency management algorithms to manage failures at the component and system level, and a software logging and display environment that enables rapid assessment of performance during testing. The system successfully completed several runs in the National Qualifying Event, but encountered a combination of sensing and control issues in the Grand Challenge Event that led to a critical failure after traversing approximately 8 miles. © 2006 Wiley Periodicals, Inc.

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