Update README.md with lifecylce node info and fix minor typos

README.md

@@ -3,15 +3,14 @@
 ![](https://github.com/open-rmf/rmf_obstacle_detectors/workflows/style/badge.svg)
 
 Packages that infer and react to the presence of obstacles from sensor inputs.
-  - [rmf_human_detector](#rmf_human_detector)
-  - [rmf_human_detector_oakd](#rmf_human_detector_oakd)
-  - [rmf_obstacle_detector_laserscan](#rmf_obstacle_detector_laserscan)
-  - [rmf_obstacle_ros2](#rmf_obstacle_ros2)
+- [rmf_human_detector](#rmf_human_detector)
+- [rmf_human_detector_oakd](#rmf_human_detector_oakd)
+- [rmf_obstacle_detector_laserscan](#rmf_obstacle_detector_laserscan)
+- [rmf_obstacle_ros2](#rmf_obstacle_ros2)
 
 ## rmf_obstacle_detector_laserscan
 ![](../media/rmf_obstacle_detector_laserscan.gif)
 
-
 A ROS 2 node that subscribes to `LaserScan` messages and publishes obstacles to `/rmf_obstacles`.
 
 The node is implemented as an [`rclcpp::lifecycle_node`](https://github.com/ros2/demos/tree/rolling/lifecycle) node where upon activation, it calibrates the surroundings based on the range values in the initial few `LaserScan` messages.
@@ -22,18 +21,19 @@ To run
 ```
 ros2 run rmf_obstacle_detector_laserscan laserscan_detector
 ```
+
 To configure and Activate
 ```
 #to configure
 ros2 lifecycle set /laserscan_obstacle_detector configure
 ```
+
 ```
 #to activate
 ros2 lifecycle set /laserscan_obstacle_detector activate
 ```
 
-
->Note: The node can also be loaded into a ROS 2 component container as a plugin (`LaserscanDetector`)
+> Note: The node can also be loaded into a ROS 2 component container as a plugin (`LaserscanDetector`)
 
 The node accepts the following parameters
 | Parameter Name | Description | Default Value |
@@ -62,31 +62,27 @@ The node has several configurable parameters documented in the [launch file](rmf
 The most important is `blob_path` as it holds the absolute path to the NN model for inference. See `depthai` documentation for more information on how the various pre-trained models can be obtained.
 It is recommended to use the [blobconverter](https://github.com/luxonis/blobconverter/) tool to obtain the `mobilenet-ssd_openvino_2021.4_6shave.blob` blob for inference.
 
-
 To visualize the detection frames, set `debug:=True`. Note: Frames will only be visualized when a human is actively detected.
 
 For more information on setup and troubleshooting see [here](rmf_human_detector_oakd/README.md)
 
 ## rmf_human_detector
 ![](../media/rmf_human_detector.gif)
 
-A ROS 2 node that subscribes to `sensor_msgs::Image` messages published by a monocular camera and runs `Yolo-V4` to detect the presence of humans. The relative pose of the humans with respect to the camera frame is estimated based on heuristics that can be configured through ROS 2 params.
+A ROS 2 node that subscribes to `sensor_msgs::Image` messages published by a monocular camera and runs `Yolo-V4` to detect the presence of humans. The relative pose of the humans with respect to the camera frame is estimated based on heuristics that can be configured through ROS 2 parameters.
 
 The use case for this node would be to detect crowds from existing CCTV cameras or vision sensors in the facility.
 
 To run:
-
 ```bash
 ros2 launch rmf_human_detector human_detector_launch.py
 ```
 
 ## rmf_obstacle_ros2
 The `rmf_obstacle_ros2` package contains ROS 2 nodes that react to the presence of obstacles.
 
-
 At present the `lane_blocker_node` is available which subscribes to `/rmf_obstacles`, and checks whether
-any of the obstacles intersect with any of the lanes across fleet navigation graphs.
-
+any of the obstacles intersects with any of the lanes across fleet navigation graphs.
 If a new intersection is determined, the lanes for the corresponding fleets are closed.
 Previously closed lanes are also opened once the obstacles no longer intersect with the lanes.