Navigation stack planning global path through large unknown wall segments of map

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I'm running a Turtlebot type platform with a fairly large map of a building, about 70x100m. Using the navigation stack to go to waypoints works almost all the time when the destination is close by and the path is relatively straight, but I'm getting some weird behaviors when planning long paths across the shape of the map.

The map is a U shape, when trying to plan a plan from one side to the other, the global path basically takes a straight line across the U, going through both walls, and completely ignoring the known open space available to it on the map.

The other interesting behavior I saw was it chose to navigate down one hallway instead of crossing to the other, in this situation below. This photo isn't the path from when it had that issue, but instead of cutting across and going down the right side, the robot went down the left hallway then stopped roughly perpendicular to the destination and tried to plan through the wall, which it obviously couldn't.

It seems to me that, given there's a global map with known open space, the navigation stack should be putting out a path that goes through that empty space, following the hallways rather than cutting across. It also seems to me that the stack shouldn't be trying to plan through a known blocked space, the wall on both sides. Am I correct in these two, or am I misunderstanding how the stack works?

One suggestion from another question suggested turning off allow_unknown in the path planner, but there are areas of my map, mainly doorways, where I need to use unknown space to get to some destinations.

I've put my configurations below, but I'm a bit at a loss of what to do to resolve this... Any suggestions?

base_local_planner_params.yaml:

TrajectoryPlannerROS:
  max_vel_x: 0.45
  min_vel_x: 0.1
  max_vel_theta: 1.5
  min_in_place_vel_theta: 0.2

  acc_lim_theta: 3.2
  acc_lim_x: 2.5
  acc_lim_y: 2.5

  holonomic_robot: false

  yaw_goal_tolerance: 0.3
  xy_goal_tolerance: 0.2
  latch_xy_goal_tolerance: true

costmap_common_params.yaml:

obstacle_range: 2.5
raytrace_range: 15.0
#footprint: [[x0, y0], [x1, y1], ... [xn, yn]]
robot_radius: 0.22
inflation_radius: 0.22

observation_sources: laser_scan_sensor

laser_scan_sensor: {sensor_frame: laser, data_type: LaserScan, topic: scan, marking: true, clearing: true}

recovery_behaviors.yaml:

reovery_behavior_enabled: true
recovery_behaviors:
  - name: 'conservative_reset'
    type: 'clear_costmap_recovery/ClearCostmapRecovery'
  - name: 'aggressive_reset'
    type: 'clear_costmap_recovery/ClearCostmapRecovery'

conservative_reset:
  reset_distance: 3.0

aggressive_reset:
  reset_distance: 0.0

global_costmap_params.yaml:

global_costmap:
  global_frame: /map
  robot_base_frame: base_link
  update_frequency: 5.0
  static_map: false
  width: 200

local_costmap_params.yaml:

local_costmap:
  global_frame: odom
  robot_base_frame: base_link
  update_frequency: 5.0
  publish_frequency: 2.0
  static_map: false
  rolling_window: true
  width: 6.0
  height: 6.0
  resolution: 0.05
  obstacle_layer:
    observation_sources: "base_scan"
    base_scan: {data_type: LaserScan, sensor_frame: laser, clearing: true, marking: true, topic: /scan}
  height: 200

The robot is running Ubuntu 16.04 with Kinetic onboard an Odroid XU4.