Neural Sensor Fusion for Spatial Visualization on a Mobile Robot

An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B 14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots are retrospectively associated with the distance to the wall, provided by on~ board odomctry as the robot travels in a straight line. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. The neural network effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.Office of Naval Research and Naval Research Laboratory (00014-96-1-0772, 00014-95-1-0409, 00014-95-0657

Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally

Fast processing of grid maps using graphical multiprocessors

Grid mapping is a very common technique used in mobile robotics to build a continuous 2D representation of the environment useful for navigation purposes. Although its computation is quite simple and fast, this algorithm uses the hypothesis of a known robot pose. In practice, this can require the re-computation of the map when the estimated robot poses change, as when a loop closure is detected. This paper presents a parallelization of a reference implementation of the grid mapping algorithm, which is suitable to be fully run on a graphics card showing huge processing speedups (up to 50×) while fully releasing the main processor, which can be very useful for many Simultaneous Localization and Mapping algorithms

Multi-Robot FastSLAM for Large Domains

For a robot to build a map of its surrounding area, it must have accurate position information within the area, and to obtain accurate position information within the area, the robot needs to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these maps poses a serious limitation to the algorithm\u27s capability when the problem space is large. The problem will only get worse if the algorithm is adapted to multi-robot mapping. This thesis presents an alternate mapping algorithm that extends the single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations (ASR) to represent the world. But each particle still maintains a local grid to map its vicinity and periodically this grid map is converted into an ASR. An ASR expresses a world in polygons requiring only a minimal amount of memory space. By using this altered mapping strategy, the problem faced in FastSLAM when mapping a large domain can be alleviated. In this algorithm, each robot maps separately, and when two robots encounter each other they exchange range and odometry readings from their last encounter to this encounter. Each robot then sets up another filter for the other robot\u27s data and incrementally updates its own map, incorporating the passed data and its own data at the same time. The passed data is processed in reverse by the receiving robot as if a virtual robot is back-tracking the path of the other robot. The algorithm is demonstrated using three data sets collected using a single robot equipped with odometry and laser-range finder sensors

Extending the Occupancy Grid Concept for Low-Cost Sensor Based SLAM

The simultaneous localization and mapping problem is approached by using an ultrasound sensor and wheel encoders. To be able to account for the low precision inherent in ultrasound sensors, the occupancy grid notion is extended. The extension takes into consideration with which angle the sensor is pointing, to compensate for the issue that an object is not necessarily detectable from all position due to deficiencies in how ultrasonic range sensors work. Also, a mixed linear/nonlinear model is derived for future use in Rao-Blackwellized particle smoothing