Odometry-Less Indoor Dynamic Object Detection and Localization with Spatial Anticipation

The continually evolving image segmentation methods in computer vision can further broaden the cognitive abilities of the robot. As humans, we won't judge if the object is moving by accuracy speed estimation but through semantic information based on visual input. With the existing video segmentation method, the robot can pay more attention to the foreground objects, which are more important for path planning and navigation in most cases. The proposed methods try to deploy the first-person view segmentation method with an RGBD sensor and get the location information and locally egocentric map. The error of the segmentation methods will cause unexpected objects in the egocentric map, and the self-occlusion will influence the location and distance estimation. To further improve the methods, a spatial anticipate unit is embedded into the framework. We improve 5.6% the localization accuracy(RMSE), 12.9% the distance estimation(MAE) with the trained object, and 28.9% the recall for the trained object. The methods also show the margin on the untrained object dataset for similar objects.