Bayesian Hilbert Maps on TPU

• Setup
• We use RPLidar

To convert rplidar raw data (offline) to BHM compatible csv, run rplidar_to_bhm_convert_offline.py . Data will be saved in datasets (and datasets/figs/). To run BHM, run main_bhm_pytorch.py . Parameters of BHM can be set in the yaml files in the config folder. To convert BHM compatible csv to an updatable Bayesian Hilbert Map, run rplidar_to_bhm_convert_online.py To create Bayesian Hilbert Maps from the Lidar directly, run rplidar_to_bhm_live_fromlidar2.py

Requirements

• TensorFlow Lite
• PyTorch
• Matplotlib
• Pandas
• Numpy

Installation

• link websites for installation
• TensorFlow: https://qengineering.eu/install-tensorflow-1.15.2-on-raspberry-pi-4.html
• PyTorch: https://gist.github.com/akaanirban/621e63237e63bb169126b537d7a1d979

Run

In the project directory, you can run:

rplidar_to_bhm_live_fromlidar2.py

To run the Edge TPU computer vision code go to the edge-tpu-tiny-yolo_ directory and run:

inference.py --model quant_coco-tiny-v3-relu_edgetpu.tflite --anchors tiny_yolo_anchors.txt --classes coco.names --cam -t 0.1 --edge_tpu --quant