Collaborative Multi-vehicle SLAM with moving object tracking
MetadataShow full item record
Although simultaneous localization and mapping (SLAM) algorithms are widely appreciated in mobile robot navigation, they can be further improved to suit practical applications in dynamic environmental conditions. One such important improvement is the detection and tracking of moving objects present in the sensor field of view (FOV). In this paper we propose to extend our recently introduced CollaborativeMulti-vehicle SLAM (CMSLAM) solution based on the random finite set (RFS) representation of the feature map and measurements, by tracking both static and dynamic features. We represent static features observed during the SLAM process, along with dynamic features present in the current sensor FOV, as an augmented RFS. The corresponding probability density is propagated using a Bayes recursion, from which the static feature map and the estimates of dynamic feature locations can be obtained. Measurement update in the CMSLAM process is carried out only using the static feature map to take advantage of obvious accuracy improvements.
Showing items related by title, author, creator and subject.
LeCras, Jared; Paxman, Jonathan; Saracik, Brad (2013)A dynamic light source poses significant challenges to vision based localization algorithms. There are a number of real world scenarios where dynamic illumination may be a factor, yet robustness to dynamic lighting is not ...
LeCras, Jared; Paxman, Jonathan; Saracik, Brad (2011)Localization in dynamically illuminated environments is often difficult due to static objects casting dynamic shadows. Feature extraction algorithms may detect both the objects and their shadows, producing conflict in ...
Le Cras, Jared R (2012)This thesis describes the development and implementation of a multisensor large scale autonomous mapping system for surveying tasks in underground mines. The hazardous nature of the underground mining industry has resulted ...