A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots

Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF algorithm that is capable of processing two-dimensional maps containing up to 1.8 k features at real time (14 Hz), a three-fold improvement over a Pentium M 1.6 GHz, and a 13-fold improvement over an ARM920T 200 MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

Archaeological Mounds in Marajo Island in Northern Brazil: A Geological Perspective Integrating Remote Sensing and Sedimentology

Earthen mounds with archaeological artifacts have been well known in Marajo Island since the 19th century. Their documented dimensions are impressive, e.g., up to 20m high, and with areas large as 90 ha. The mounds, locally known as lesos, impose a significant. relief on the very low-lying landscape of this region, which averages 4 to 6 in above present. sea level. These features have been traditionally interpreted as artificial constructions of the Marajoara culture, designed for defense, cemetery purposes, or escape from flooding. Here, we provide sedimentological and geomorphological data that suggest an alternative origin for these structures that is more consistent with their monumental sizes. Rather than artificial, the Marajoara tesos seem to consist of natural morphological features related to late Pleistocene and Holocene fluvial, and possibly tidal-influenced, paleochannels and paleobars that became abandoned as depositional conditions changed through dine. Although utilized and modified by the Marajoara since at least 2000 years ago, these earthen mounds contain a significant non-anthropogenically modified sedimentary substratum. Therefore, the large Marajoara tesos are not entirely artificial. Ancient, Marajoara cultures took advantage of these natural, preexisting elevated surfaces to base their communities and develop their activities, locally increasing the sizes of these fluvial landforms. This alternative interpretation suggests less cumulative labor investment, in the construction of the mounds and might. have significant implications for reconstructing the organization of the Marajoara culture. (C) 2009 Wiley Periodicals, Inc.