Multiple Integrated Navigation Sensors for Improved Occupancy Grid Fastslam
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An autonomous vehicle must accurately observe its location within the environment to interact with objects andaccomplish its mission. When its environment is unknown, the vehicle must construct a map detailing its surroundingswhile using it to maintain an accurate location. Such a vehicle is faced with the circularly defined SimultaneousLocalization and Mapping (SLAM) problem. However difficult, SLAM is a critical component of autonomous vehicleexploration with applications to search and rescue. To current knowledge, this research presents the first SLAM solutionto integrate stereo cameras, in...
An autonomous vehicle must accurately observe its location within the environment to interact with objects andaccomplish its mission. When its environment is unknown, the vehicle must construct a map detailing its surroundingswhile using it to maintain an accurate location. Such a vehicle is faced with the circularly defined SimultaneousLocalization and Mapping (SLAM) problem. However difficult, SLAM is a critical component of autonomous vehicleexploration with applications to search and rescue. To current knowledge, this research presents the first SLAM solutionto integrate stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor(MINS) path. The implementation combines the MINS path with LIDAR to observe and map the environment using theFastSLAM algorithm. In real-world tests, a mobile ground vehicle equipped with these sensors completed a 140 meterloop around indoor hallways. This SLAM solution produces a path that closes the loop and remains within 1 meter oftruth, reducing the error 92% from an image-inertial navigation system and 79% from odometry FastSLAM.
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