Custom Dataset Evaluation about occupancyanticipation

[jaidevshriram]

Thanks! I think I got it working now. I was interested in using just the OccAnt model, not the mapper, so using just RGB, Depth and ego map as inputs worked.

[srama2512]

Hi @jaidevshriram,

The model currently depends on having RGB-D + odometry inputs. I don't see a way to get around having no pose inputs whatsoever. The pre-trained models are trained to handle a specific noise model in the odometer sensor (see code here). Assuming that you have perfect pose estimates or noise similar to the above model, you would just have to ensure that the inputs are in the expected format to the mapper (see here). This specific function might offer the best insights for what you're trying to achieve. The interface to the mapper module was designed to be easy to understand. In your case, you would have to input the following variables to update the map at time t:

            "rgb_at_t_1" - RGB image at t-1
            "depth_at_t_1" - Depth image at t-1
            "ego_map_gt_at_t_1" - Egocentric local map at t-1
            "pose_at_t_1" - Estimated agent world pose at t-1 (relative to starting position) -- this may have noise in it
            "pose_hat_at_t_1" - Agent corrected pose at t-1 --- the agent corrects for the noise (if trained to do so)
            "map_at_t_1" - Full world map from 0 to t-1 (agent starts at the center of this map facing north)
            "rgb_at_t" - RGB image at t
            "depth_at_t" - Depth image at t
            "ego_map_gt_at_t" - Egocentric local map at t
            "pose_at_t" - Estimated agent world pose at t --- this may have noise in it

This should be a good starting point. I hope you can develop the code based on this. Feel free to bring up any specific questions you have here.

[srama2512]

Glad to hear that @jaidevshriram . I'm closing this for now. Please feel free to open it in case you have further questions.